a cold start context-aware recommender system...

Research ArticleA Cold Start Context-Aware Recommender System forTour Planning Using Artificial Neural Network and CaseBased Reasoning

Zahra Bahramian1 Rahim Ali Abbaspour1 and Christophe Claramunt2

1School of Surveying and Geospatial Engineering College of Engineering University of Tehran North Kargar AveAfter Jalal Al Ahmad Crossing Tehran 11155-4563 Iran2Naval Academy Research Institute Brest France

Correspondence should be addressed to Rahim Ali Abbaspour abaspourutacir

Received 28 April 2017 Revised 19 June 2017 Accepted 28 June 2017 Published 10 September 2017

Academic Editor Salvatore Carta

Copyright copy 2017 Zahra Bahramian et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

Nowadays large amounts of tourism information and services are available over the Web This makes it difficult for the user tosearch for some specific information such as selecting a tour in a given city as an ordered set of points of interest Moreover theuser rarely knows all his needs upfront and his preferences may change during a recommendation process The user may also havea limited number of initial ratings and most often the recommender system is likely to face the well-known cold start problemTheobjective of the research presented in this paper is to introduce a hybrid interactive context-aware tourism recommender systemthat takes into account userrsquos feedbacks and additional contextual information It offers personalized tours to the user based on hispreferences thanks to the combination of a case based reasoning framework and an artificial neural networkThe proposedmethodhas been tried in the city of Tehran in IranThe results show that the proposedmethod outperforms current artificial neural networkmethods and combinations of case based reasoning with 119896-nearest neighbor methods in terms of user effort accuracy and usersatisfaction

1 Introduction

Nowadays huge amounts of tourism information are avail-able over the Web Such information may be helpful for agiven user who plans to visit an unknown region Howeverit is often overwhelming and time consuming for the user tolook through all the available information in order to find outsome relevant places of interest and to organize them in a sortof well-defined tour Hence irrelevant information shouldbe filtered and personalized options should be extractedaccording to the userrsquos preferences

Recommender systems (RSs) can be roughly defined asinformation filtering and decision support tools that provideproducts and services that match userrsquos preferences [1ndash3]Such recommender systems limit the information over-load provide personalized content and increase systemsusability as adapted to the problem [4ndash8] Most of current

recommender systems assume that userrsquos preferences do notchange whatever the context [9 10] However in the case ofa recommender system applied to the tourism domain userrsquospreferences can be affected by many contextual factors thatshould be particularly considered when providing recom-mendations For instance when considering the user interestfor a given point of interest (POI)many additional contextualparameters such as userrsquos own mobility previous historyand the timing of a recommendation should be consideredas important contextual information Hence in order toprovide satisfactory services to the final users a context-aware tourism recommender system should be used in orderto automatically adapt the recommendations to changingcontexts [11ndash13] In other words context-aware tourism rec-ommender systems should incorporate contextual informa-tion into the recommendation process to appropriatelymodeland predict the user preferencesMoreover they should adapt

HindawiMobile Information SystemsVolume 2017 Article ID 9364903 18 pageshttpsdoiorg10115520179364903

2 Mobile Information Systems

these recommendations not only to the userrsquos preferencesbut also to the contextual situation to generate more relevantsuggestions

However and to the best of our knowledge previouscontext-aware tourism recommender systems developed sofar have not completely addressed these issues [9 11ndash28] Oneweakness of these researches is that they often suggest to agiven user a list of POIs which are specific tourist attractionsthat a user may find interesting such as a historical or culturalbuildings or any places worth being visited rather than atour as an ordered set of POIs Moreover and despite thefact that collaborative recommendations are often providedbased on other users self-learning processes based on thecontext and assessment of a given user are not taken intoaccount Also another common drawback of related context-aware tourism recommender systems is that they generallyoffer suggestions to a given user in one single session onlyIndeed most of the users rarely know all their needs atfirst Therefore initial recommendations are likely to be notalways satisfactory Moreover most current context-awaretourism recommender systems often suffer from the coldstart problem which occurs when a new user with little priorratings has been registered to the system making it difficultto suggest meaningful recommendations for that new userThey also assume that the userrsquos preferences are stable and donot take into account userrsquos preferences variations over time

In order to overcome these problems several researchersapply case based reasoning (CBR) [29ndash31] to develop casebased recommender systems that present userrsquos preferenceas a query item that he likes and each item case as a setof attributes [32ndash34] These systems retrieve items that aresimilar to the userrsquos query case The main advantages of CBRis that (a) it can be directly applied to learn the user profileat the user modeling level (b) it has the ability to learn thedynamic behavior of the system over time and (c) it canprovide effective explanation mechanisms when a sufficientnumber of previous cases are available

However an important issue still left for CBR is howto predict toursrsquo ratings in order to recommend the mosttop rated tours to a user In other words some features aremore important than the others and then appropriate featureweighting should be executed before any rating prediction inorder to take into account each feature relative importanceIn existing case based recommender systems the 119896-nearestneighbor (KNN) method [35] is widely used in ratingprediction [32 33] but it assumes that all selected features areequally important this being not true in many cases as forinstance each POI type is likely to have a specific interestvalue Another open problem is how to progressively adaptthe user model based on the information derived from thedifference identified between a selected tour and the othertours in order to suggest a tour in a next cycle

These problems lead us to proposing a hybrid methodthat combines an artificial neural network (ANN) [36 37]with CBR Our research develops a cold start context-awarerecommender system applied to the tourism domain thatoffers personalized tours to the user based on his preferencesand some contextual information Our objective is to over-come the above-mentioned problems by considering userrsquos

feedback and applyingCBRusing anANNThe contributionsof this paper include (1) providing a tourism recommendersystem that considers contextual situations in the recommen-dation process (2) proposing an interactive framework thatconsiders userrsquos feedback to (a) suggest tours to a user withlimited knowledge about his own needs (b) overcome thecold start problem which occurs when a new user with littleprior ratings uses the system and (c) take into account userrsquospreferences variations (3) recommending some tours to theusers including both POIs and routes between them over thestreet network and (4) proposing tours to the user takinginto account only his own preferences and feedbacks withoutconsidering the preferences and feedbacks of other users

The rest of this paper is as follows Section 2 introducesseveral concepts from recommender to context-aware rec-ommender systems It also briefly reviews context-awarerecommender systems applied to the tourism domain Thenit presents the principles behind CBR and ANN and a briefintroduction to semantic similarity principles The proposedmethod and its implementation results are explained inSections 3 and 4 respectively Finally Section 5 providesconclusions and outlines future works

2 Literature Review

This section describes several basic concepts on recom-mender and context-aware recommender systems with aspecific focus on the tourism domain The main principlesbehind CBR ANN and property-based semantic similarityare briefly discussed

21 Recommender Systems Recommender systems (RSs)select some items proven to better match userrsquos preferencesand reduce information overload [4 5 38] Without lossof generality let us categorize current recommendationapproaches towards content-based recommender systemscollaborative recommender systems knowledge-based rec-ommender systems and hybrid recommender systems Theyare discussed below

Content-based recommender systems derive the similarityof features with respect to userrsquos preferences and select theones that best match userrsquos preferences [39] Collaborativerecommender systems make recommendations based on thepreferences of similar users [3 40] Knowledge-based recom-mender systems apply reasoning mechanisms to make appro-priate suggestions according to userrsquos preferences as relatedto items properties [41ndash43] An interactive recommendersystem can be also considered as a knowledge-based rec-ommender system Navigation-by-proposing is one formof interactive recommender systems that are based onpreference-based feedback [44ndash46]Hybrid recommender sys-tems apply a combination of current recommendation tech-niques The main idea behind hybrid recommender systemsis to exploit the advantages of one technique to compensatefor the shortcomings of another and improve the overallperformance [47] A metalevel hybrid recommender systemis a class of hybrid recommender system that uses the modellearned by one recommender system as an input for another[47]

Mobile Information Systems 3

22 Context-Aware Recommender Systems Context may bedefined as any additional information that can be used tocharacterize the location and properties of a given entity Anentity is a person place or object that is considered relevantto the interaction between a user and an application includ-ing the user and applications themselves [48] In context-aware recommender systems (CARSs) the ratings are as afunction of not only items and users but also of contextualattributes whose role can be roughly formalized as follows[10 49]

119877 user times item times context 997888rarr rating (1)

where user item and context denote the considered model-ing dimensions while rating returns the value of the consid-ered item for that user according to the context

23 Context-Aware Recommender Systems in the TourismDomain Several context-aware recommender systems havebeen developed so far in the tourism application domainOne of the early mobile examples in the tourism domain isthe Cyberguide project [14] The Cyberguide offers severaltour guide prototypes for handheld devices where contextualinformation on the user surroundings is provided on requestContextual knowledge of the userrsquos current and past locationsare taken into account to generate user daily historiesGeoWhiz [22] proposes a mobile restaurant recommendersystem that uses a collaborative filtering approach basedon user locations Magitti [16] offers a mobile leisure guidesystem where a user context is detected It infers current andpossible future leisure activities of interest and recommendssuitable venues to the users (eg stores restaurants parksand cinemas) It offers a sort of context-aware system thatconsiders time and location and additional context data suchas weather and venues opening hours as well as previoususer patterns It is also activity-aware and filters items notmatching the users or explicitly specified activities Reference[9] develops a location-based service recommendationmodel(LBSRM) that integrates location and user preferences to rec-ommend the most appropriate hotels to the user User pref-erences are taken into account thanks to an adaptive methodincluding long-term and short-term preference adjustments

Reference [15] proposes a hybrid mobile context-awaresystem to personalize POIs recommendations POIs locatedwithin a radius around the userrsquos location and based onthe userrsquos trajectory and speed are suggested Contextuallyfiltered POIs are filtered and selected according to the userrsquospreferences The mobile tourism recommendation system(MTRS) developed by [20] delivers several personalizedrecommendation services to mobile users considering con-textual information such as the userrsquos location current timeweather conditions and POIs already visited by the usersTheiTravel system [27] applies a collaborative filtering approachThe similarities between users are evaluated using the userrating lists Nearby users are detected and rating data areexchanged using mobile peer-to-peer communications Irsquomfeeling Loco [13] is a ubiquitous location-based sites recom-mendation It uses the userrsquos preferences derived from theFoursquare location-based social network the userrsquos location

the used transport modes (eg walking bicycle or car) andthe current user feeling in recommendation process ReRex[11] assesses and models the relationship between contextualfactors and item ratings Using a predictive model based onmatrix factorization ReRex recommends POI and suggestsa complete itinerary according to a particular itineraryrsquoscontext

SigTurE-Destination [25] provides personalized recom-mendations applied to tourism activities It considers differ-ent kinds of data including demographic information andtravel motivations and takes into account user actions andratings to generate recommendations The approach is basedon a tourism domain ontology that favors the classification ofuser activities The reasoning process also combines content-based and collaborative recommendations TRIPBUILDER[17] suggests personalized city sightseeing tours POIs arederived from Wikipedia and illustrated by Flickr georef-erenced photos Tours are inferred from POI travel timesas inferred from Google maps and a Traveling SalesmanProblem algorithm that minimizes travel times and maxi-mizes userrsquos interests POIs are categorized and semanticallydescribed by their popularity and average visiting time andfurther enriched by patterns of tourist movements

In [26] tourist locations of interest are derived fromtheir proximity to Flickr geotagged photos semanticallycontextualized according to their related season and weathercontext and finally rated in order to recommend somePOIs Travel histories are then derived and annotated ina sort of topic-based model this favoring the search foruser similarities Reference [21] develops a probabilistic topicmodel to recommend POIs based on userrsquos interests as wellas the ones of socially related users

POST-VIA 360 [19] assists tourists in previsit during-visit and postvisit stages of their travels by means of acustomer relationshipmanagement (CRM) approach Geolo-cated POIs are classified and recommended thanks to an arti-ficial immune system (AIS) that successively rates the POIsaccording to user rating PlanTour [18] generates personalizedoriented multiple-day tourist plans The POIs are identifiedfrom a Minutube traveling social network and clustered overa daily basis Tourist plansmaximize the user utility and POIstravel distance Reference [28] mines multiple periods in thetime sequence to calculate the userrsquos period of arriving at acertain area Then they use item-based collaborative filteringfor recommending locations based on usersrsquo preference andthe periodic behaviors

Table 1 summarizes such context-aware recommendersystems as applied to the tourism domain according to aseries of preselected criteria The previous context-awarerecommender systems in tourism domain apply single-shotrecommendation strategy and return a single set of sugges-tions to a given user in one session onlyThey do not considerthe user feedback in an interactive recommender systemin tourism domain Moreover they are unable to proposerecommendations to a user that has limited knowledge abouthis needs or little prior ratings or changing preferencesduring the time In addition none of the previous context-aware RSs in tourism domain recommend tours as sequencesof POIs to the user


Table1Con

text-awarer

ecom

mendersystemsa

ppliedto

thetou

rism

domain

Recommender

syste

m

Con

text-awarep

roperties

RecommenderS

ystem

prop

ertie

s

Usedcontext

Con

text-aware

recommendatio

nRe

commendedIte

mRe

commendatio

ntechniqu

eUser

feedback

User

Environm

ent

Locatio

nTime

Con

textual

prefilterin

gCon

textual

postfi

lterin

gCon

textual

mod

eling

POI

Hotel

Restaurant

Leisu

reRo

ute

Tour

Con

tent-

based

Collabo

rativ

eKn

owledge-

based

Hybrid

Yes

No

Cyberguide

[14]

GeoWhiz[22]

Magitti[16]

LBSR

M[9]

MTR

S[20]

REJA

[15]

Irsquomfeeling

Loco

[13]

ReRe

x[11]

iTravel[27]

SigTurE-

Destin

ation

[25]

TRIPBU

ILDER

[17]

[26]

SocoTravele

r[21]

POST

-VIA

360

[19]

PlanTo

ur[18

]

[28]


New case

New case

Problem

Learnedcase

Solvedcased

Revisedcase

Reuse

Revise

Reta

inRetrieved

case

Precedentcases

Generalknowledge

Retrieve

Figure 1 Case based reasoning cycle

24 Case Based Reasoning Case Based Reasoning (CBR) isa machine learning method that models human reasoningprocesses for learning andproblem solvingACBR stores pastcases as a historical repository Each case consists of two partsincluding the problem description and the solutions [29 5051] The problem description represents the context that iswhen the case takes place while the solution part shows themethods applied to resolve the caseTheobjective of CBR is tosolve new problems using solutions that were used to addresssimilar problems [30 31]The structural CBR approach is onetype of case representation that represents cases according toa common structured vocabulary using predefined attributesand values [52] CBR generally involves four steps [53] (1)retrieving similar previously experienced cases (2) reusingcases by copying or integrating the emerging solutions fromthe retrieved cases (3) revising or adapting the retrievedsolutions to solve a new problem and (4) retaining a newconfirmed or validated solution (Figure 1)

25 Artificial Neural Network Artificial Neural Network(ANN) [36 37] is a branch of artificial intelligence inspiredby the biological brain It is a structure of connected nodes(ie artificial neurons) that are arranged in layers Thelinks between nodes have weights associated with themdepending on the amount of influence one node has onanother A feedforward ANN is an ANN without any cyclesin the network and propagates incoming data in a forwarddirection only A multilayer perceptron (MLP) is one type offeedforward ANNs with full connection of each layer to thenext one The MLP consists of one input layer one or morehidden layers and one output layer Nodes in the input layerrespond to data that is fed into the network while outputnodes produce network output values Hidden nodes receivethe weighted output from the previous layerrsquos nodes Figure 2shows the 119899inputminus119899hiddenminus119899output (119899input denotes input neurons119899hidden denotes hidden neurons and 119899output denotes outputneurons) architecture of a feedforwardmultilayer perceptronANN

Input layer Hidden layer Output layer

x1

x2

xnＣＨＪＯＮ

a3w2

a2w1

a1

Figure 2 Structure of a feedforward neural network

The output of unit 119894 in layer 119897 + 1 is119886119897+1 (119894) = 119891119897( 119899119897sum

119895=1

119908119897 (119894 119895) 119886119897 (119895) + 119887119897 (119894)) (2)

The most common concrete algorithm for learning MLPis the backpropagation algorithm In the backpropagationalgorithm the computed output values are compared withthe correct output values to compute the value of errorfunctionThen the error is fed back through the networkThederivatives of the error function with respect to the networkweights are calculated The weights of each connection areadjusted using gradient descent method such that the valueof the error function decreases This process is repeated untilthe network converges to a state with small error

Different training algorithms have been so far appliedto train a backpropagation MLP Each algorithm adjusts theANN as follows

119908119896 = 119908119896minus1 + Δ119908119896 (3)

where 119896 is the index of iterations 119908119896 is the vector of weightsin 119896 iteration of training algorithm and Δ119908119896 is the vector ofweights changes that is computed by each training algorithmincluding gradient descent with momentum and adaptivelearning rate backpropagation (GDX) resilient backpropaga-tion (RP) conjugate gradient backpropagation with Fletcher-Reeves updates (CGF) conjugate gradient backpropagationwith Polak-Ribiere updates (CGP) conjugate gradient back-propagation with Powell-Beale restarts (CGB) scaled conju-gate gradient backpropagation (SCG) BFGS quasi-Newtonbackpropagation (BFG) one-step secant backpropagation(OSS) and Levenberg-Marquardt backpropagation (LM)(Table 2)

26 Property-Based Semantic Similarity The semantic simi-larity of two objects can be evaluated based on their commonfeatures or the lack of distinctive features of each objectaccording to a domain ontology A feature-based semanticsimilaritymeasure is one of themain approaches that evaluatethe semantic similarity among concepts [54] It considers thefeatures of the concepts considered according to the ontologyand evaluates the common and distinct features to derive asimilarity measure

Consider objects 1198741 and 1198742 with 119899119901 properties (119901119902 119902 =1 2 119899119901) Using Tverskyrsquos formulation the similarity


Table 2 Algorithms for training backpropagation multilayer perceptron neural network

Trainingalgorithm Description

GDX

It renovates weight and bias values conceding to the gradient descent and current trends in the error surface (Δ119864119896) withadaptive training rate

Δ119908119896 = 119901Δ119908119896minus1 + 120572119901Δ119864119896Δ119908119896

RP

It considers only the sign of the partial derivative to determine the direction of the weight update and multiplies it with the stepsize (Δ 119896) Δ119908119896 = minus sign(Δ119864119896Δ119908119896 )Δ 119896

CGF

The vector of weights changes is computed asΔ119908119896 = 120572119896119901119896where 119901119896 is the search direction that is computed as

119901119896 = minus1198920 119896 = 0minus119892119896 + 120573119896119901119896minus1 119896 = 0

where the parameter 120573119896 is computed as

120573119896 = 119892119879119896 119892119896119892119879119896minus1119892119896minus1

CGP




120573119896 = 119892119879119896minus1119892119896119892119879119896minus1119892119896minus1

CGBThe search direction is reset to the negative of the gradient only if there is very little orthogonality between the present gradientand the past gradient as explained in this condition|119892119879119896minus1119892119896| ge 021198921198962

SCG

It denotes the quadratic approximation to the error 119864 in a neighborhood of a point 119908 by119864119902119908 (119910) = 119864 (119908) + 1198641015840(119908)119879119910 + 1211991011987911986410158401015840 (119908) 119910

To minimize 119864119902119908(119910) the critical points for 119864119902119908(119910) are found as the solution to the following linear system1198641015840119902119908(119910) = 11986410158401015840(119908)119910 + 1198641015840(119908) = 0

BFG

The vector of weights changes is computed asΔ119908119896 = minus119867119879119896 119892119896where119867119896 is the Hessian (second derivatives) matrix approximated by 119861119896 as

119861119896 = 119861119896minus1 + 119910119896minus1119910119879119896minus1119910119879119896minus1Δ119909119896minus1 minus

119861119896minus1Δ119909119896minus1(119861119896minus1Δ119909119896minus1)119879Δ119909119879119896minus1119861119896minus1Δ119909119896minus1

OSS

It generates a sequence of matrices 119866(119896) that represents increasingly accurate approximations to the inverse Hessian (119867minus1) Theupdated expression uses only the first derivative information of 119864 as follows

119866(119896+1) = 119866(119896) + 119901119901119879119901119879V minus(119866(119896)V) V119879119866(119896)

V119879119866(119896)V + (V119879119866(119896)V) 119906119906119879where 119901 = 119908(119896+1) minus 119908(119896)

V = 119892(119896+1) minus 119892(119896)119906 = 119901119901119879V minus 119866(119896)V

V119879119866(119896)V and 119892 is the transfer function It does not store the complete Hessian matrix and assumes that the previous Hessian was theidentity matrix at each iteration

LM

The vector of weights changes is computed asΔ119908119896 = minus (119869119879119896 119869119896 + 120583119868)minus1 119869119896119890119896where combination coefficient 120583 is always positive 119868 is the identity matrix 119869119896 is Jacobian matrix (first derivatives) and 119890119896 isnetwork error


between two objects is given by a feature-based semanticsimilarity calculation method as follows [54 55]

SIM (1198741 1198742) =119899119901sum119902=1

119908119902SIM119901119902

SIM119901119902 = 120572CF119901119902 (1198741 1198742)120573CF119901119902 (1198741) + 120574CF119901119902 (1198742) + 120572CF119901119902 (1198741 1198742) (4)

where CF119901119902(1198741) CF119901119902(1198742) and CF119901119902(1198741 1198742) denote thecommon features of 1198741 and 1198742 distinctive features of 1198741and distinctive features of 1198742 with respect to property 119901119902respectively119908119902 assigns the relevance amount of the property119901119902 and 120572 120573 120574 isin R are constants that value the respectiveimportance of the features considered

3 Proposed Methodology

The objective of this paper is to consider the user feedbacksanddevelop a hybrid interactive context-aware recommendersystem applied to the tourismdomain that offers personalizedtours to a user based on his preferences The proposedmethod combines an interactive tourism recommender sys-tem designed as a knowledge-based recommender systemwith a content-based tourism recommender system thanksto applying CBR and ANN

The proposed method takes into account contextualinformation in the modeling process as a contextual mod-eling approach whose objective is to recommend the mostappropriate tours to a user It takes into account contextualinformation as follows

(i) POIs locations in the userrsquos environment(ii) POIs opening and closing times(iii) the street network(iv) the tours already visited by the user denoted as his

mobility history(v) the distance traveled by the user on the street network

Moreover the rating assigned to the visited tours by the userand userrsquos feedbacks are considered as user preferences andfeedbacks respectively The following subsections developthe problem formulation and the proposed context-awaretourism recommender system

31 Problem Formulation In this paper each tour is modeledas a sequence of POIs A POI is a specific tourist attraction orplace of interest that a usermay find of value Pois denotes theset of all POIs located in the case area as

Pois = poi1 poi2 poi119899poi (5)

where 119899poi is the total number of POIs that are located in thecase area

The proposed method considers 119899type POI types such asrestaurant and cultural place types Each POI belongs to one

of these types Let Types denote the set of all POI typesavailable in the case area as

Types = tp1 tp2 tp119899type (6)

The function FuncType returns the type of a given POI as

FuncType Poi 997888rarr Type (7)

A Tour119894 is a nonempty nonrepetitive sequence of POIs that auser visits

Tour119894 = tr1198941 tr1198942 tr119894119899 (8)

where 119899 is the number of POIs in Tour119894 and tr119894119895 is a POI (tr119894119895 isinPois 119895 = 1 2 119899) in such a way that forall119897 = 119898 tr119894119897 = tr119894119898

tm119894119895 (119895 = 1 2 119899) denotes the time that a user startsvisiting tr119894119895 Without loss of generality let us introduce aconstraint to illustrate the potential of the approach Theproposed method also assumes that the dinning time isbetween 12 am and 2 pmTherefore if the start time of visitinga POI in a tour is between 12 am and 2 pm thementioned POImust be located in ldquoRestaurantrdquo type

if 12 am le tm119894119895 le 2 pmthen FuncType (tr119894119895) = ldquoRestaurantrdquo (9)

Then Tours denotes the set of all possible tours consideringthe mentioned constraints that is given as

Tours = ⋃possible tours

Tour119894 (10)

The user can assign a rating to each visited tour which isthen stored in his history The rating values are in the unitinterval [0 1] and reflect the userrsquos need The tour with thehighest rating will be themost interesting oneThe FuncRankfunction returns Rating119894 as the rating of Tour119894

FuncRate Tours 997888rarr [0 1]Rating119894 = FuncRate (Tour119894) (11)

For each Tour119894 = tr1198941 tr1198942 tr119894119899 119875119894 is computed as

119875119894 = (1199011198941 1199011198942 119901119894119899type TourDistance119894) (12)

where 119901119894119895 (119895 = 1 2 119899type) denotes the number of POIs inTour119894 that are located in tp119895 POI type TourDistance119894 denotesthe distance traveled in Tour119894 that is the sum of distancesbetween each two consecutive POIs in Tour119894 on the streetnetwork and computed as

TourDistance119894 = 119899minus1sum119895=1

Dist (tr119894119895 tr119894(119895+1)) (13)

where Dist(tr119894119895 tr119894(119895+1)) is the shortest path between tr119894119895 andtr119894(119895+1) on the street network


32 Proposed Context-Aware Tourism Recommender SystemOur objective is to develop a context-aware tourism recom-mender system that suggests the highest rated tours to a userbased on his preferences We assume that the user does notknow a priori the types of POIs he would like to visit andthat he has little prior knowledge of possible recommendedtoursThe system asks the user to assign the starting time andthe ending time of his requested tour The proposed methodfirst considers the userrsquos mobility history and his interactionswith the system to capture userrsquos preferences The proposedinteractive tour recommender system is developed in threesuccessive steps

Step 1 The system learns the current user model and recom-mends 119899show new tours to the user based on the current usermodel and some contextual information

Step 2 The user selects (or rejects) a tour among the recom-mended tours and assigns a rating to it as a feedback Thefeedback on the selected tour elicits the userrsquos needs Thisfeedback is case based rather than feature-based

Step 3 The system revises the user model for the next cycleusing information about the selected tour and 119899similar similartours

The recommendation process finishes either after thepresentation of a suitable tour to the user or after somepredefined iterations

In order to implement this interactive tour recommendersystem the system applies the CBR method as a knowledge-based filter It develops a structural case representation thateach case consists of a tour and its related rating Each touris considered as a problem description while its rating is theproblem solution CBR stores a history of the visited toursand their related ratings as previous casesWhen applying theretrieval and reusing stages of aCBR cycle the systemuses thepreviously visited tours and recommends the 119899show highestrated tours to the user At the revising stage of a CBR cyclethe user selects or rejects one of the recommended tours andrevises its rating by assigning a new rating to it as a feedbackIn the retaining stage of CBR cycle the system adapts the usermodel to use it in the next cycle

To overcome the mentioned problems in an interactivecase based recommender system the proposed approachcombines an ANN with CBR and actually takes into accountthe specific properties and values of each tour We introducea hybrid recommender system that applies ANN (as acontent-based recommender) and CBR (as a knowledge-based recommender) in a homogeneous approach denotedas a metalevel hybrid recommender system A feedforwardmultilayer perceptron ANN is used in the retrieval reusingand retaining phases of a CBR cycle as follows

321 Learning Current User Model and Recommending NewTours In this step the proposed ANN is applied to theretrieval and reusing phases of a CBR cycle The proposedmultilayer perceptron neural network is made of 3 layersan input layer a hidden layer and an output layer For

each visited Tour119894 the inputs of the proposed ANN are theelements of 119875119894 Therefore the input layer has (119899Type + 1)neurons The proposed ANN output is the related rating ofthe tour (Rating119894) and therefore the output layer has oneneuron The system uses the history of the visited tours andtheir related ratings to train the ANN

The ANN computes the ratings of all possible tours(Tours) and recommends the 119899show best computed rated toursto the user where 119899show is a system specified constant

322 Assigning the User Feedback The user selects (orrejects) a tour from 119899show recommended tours and assigns itsrating as a feedback This feedback is used to revise the usermodel

323 Revising the User Model according to His Feedback Inthis step the proposed ANN is still used in the retainingphase of a CBR cycle due to its strong adaptive learningability The ANN adapts the user model based on theuserrsquos feedback The similarity of the selected tours withall other tours is computed Consider two tours Tour119894 =tr1198941 tr1198942 tr119894119899 andTour119895 = tr1198951 tr1198952 tr119895119899The systemcomputes 119875119894 = (1199011198941 1199011198942 119901119894119899type TourDistance119894) and 119875119895 =(1199011198951 1199011198952 119901119895119899type TourDistance119895) In order to compute thesimilarity between Tour119894 and Tour119895 SIM(Tour119894Tour119895) thesimilarity between 119875119894 and 119875119895 is computed using Tver-skyrsquos feature-based semantic similarity method described inSection 26

SIM (Tour119894Tour119895) =119899type+1sum119902=1

119908119902SIM119901119902 SIM119901119902

= 120572CF119901119902 (Tour119894Tour119895)120573CF119901119902 (Tour119894) + 120574CF119901119902 (Tour119895) + 120572CF119901119902 (Tour119894Tour119895)sim

min (119901119894119902 119901119895119902) 119902 = 1 2 119899typemin (TourDistance119894TourDistance119895) 119902 = 119899type + 1

(14)

In this paper we assume 120572 = 120573 = 120574 = 1Therefore the system determines the 119899similar most similar

tours to the selected tour where 119899similar is a system specifiedconstant It assigns their ratings the same as the selected tourrating Using new ratings of the selected tour and its similartours the system adapts the proposed ANN and updates theweights of the proposed ANN Therefore the user model isupdated for the next cycle Accordingly the system stores theuserrsquos feedbacks as new experiences in the memory

This process is iterated until either a suitable tour ispresented to the user or a predefined number of iterations isreached where the number of iterations is a system specifiedconstant In fact too few interaction stages might lead toinaccurate user modeling and recommendations while onthe other hand interacting in too many iteration stages islikely to be a cumbersome user task In order to provide agood balance we consider 20 iteration stages This choice isrelatively similar to the ones made by related works and thatconsidered between 10 and 20 iteration stages [56ndash59]


User mobilityhistory

Userrsquos feedback

Time context

Environmentcontext

POIs context

Streets context

Start time

End time

Visited tour

Tour rating

MLP ANN

System extracts similar tours to theselected tour

System proposes a list of tours

User selects and rates one of therecommended tours

Retrieve and reuse

Revise

Retain

Stoppingcriteria

Final proposed tour

Yes

Hybrid context-aware tourismrecommender system engine

Contextualinformation

No

Restaurantopening time

nＭＢＩＱ

nＭＣＧＣＦＬ

Figure 3 Architecture of the proposed hybrid context-aware recommender system applied to the tourism domain

The proposed hybrid CBR-ANN approach is used asan interactional tour recommender system by combin-ing content-based recommender system (using ANN)and knowledge-based recommender system (using CBR)(Figure 3) As shown in Figure 3 a supporting databaserepresents the location and type of each POI streets informa-tion and restaurantsrsquo opening timesThe system specifies thenumber of tours that the system recommends to the user ateach cycle (119899show) the number of the most similar tours tothe selected tour that is used in the retaining phase (119899similar)and the number of iterations The user defines the startingand ending times of the required tour to the system andassigns a rating to the selected tour Algorithm 1 shows thepseudocode of the proposed method

4 Experimental Results and Discussion

The proposed hybrid context-aware tourism recommendersystem is evaluated according to the experimental contextof a user that wants to plan a tour The algorithm has beenimplemented by an Android-based prototype

41 Data Set The proposed hybrid context-aware tourismrecommender system suggests tours to each user who wantsto plan a tour in regions 11 and 12 of Tehran the capital city ofIran In order to develop the experimental setup the role ofthe users has been taken by 20 students in the Surveying and

Geospatial Engineering School of the University of TehranIn the two regions considered there are 55 POIs Each POIbelongs to one of the eleven POI types identified (119899type =11) including hotel cultural place museum shopping centermosque park theater cinema cafe restaurant and stadium(Figure 4)

Without loss of generality a series of assumptions havebeen considered as follows

(i) Each tour is a sequence of two to five POIs It is similarto the one made by several previous works in tourismrecommender system [25 60 61] that recommendtwo to five POIs per tour However the proposedmethod can be extended and applied to situationswith bigger number of POIs per tourwith someminoradaptations

(ii) The time that a user starts visiting a POI is either 8 am10 am 12 am 2 pm or 4 pm (tm119894119895 isin 8 am 10 am12 am 2 pm 4 pm)

(iii) The user starts to visit a POI (tr119894(119895+1)) 2 hours after hestarts to visit a previous POI (tr119894119895)

tm119894(119895+1) = tm119894119895 + 2 hours (15)

Initially each user visits 6 tours in the case area and assignsa rating to each of them by the unit interval The systemsaves these ratings in the userrsquos history To evaluate the


(1) Input CB initial is a set of visited tours and their ratings(2) Output CB net recommendation(3) CB = CB initial(4) net = Train ANN(CB) net is a trained ANN using CB(5) while simtermination criteria(6) 119877 = Reuse(CB net 119899show) System recommends the first 119899show highest

computed rated tours(7) 119878 = User Select(119877) User selects and rates one of the recommended tours(8) E = Extract(119878 119899similar) System extracts the first 119899similar prior tours which

have maximum similarities with tour 119878(9) net CB = Retain(netCB 119878 119864) System adapts CB and ANN(10) end

Algorithm 1 Pseudocode of the proposed hybrid context-aware tourism recommender system

Figure 4 Synthetic view of the case study

proposed context-aware tourism recommender system someevaluation metrics and methods should be determined

42 EvaluationMetrics andMethods In order to evaluate theproposed method the proposed system (CBR-ANN basedrecommender system) is compared to two other recom-mender systems including ANN based recommender systemand CBR-KNN based recommender system

(i) The ANN based recommender system is a single-shot content-based recommender system that uses anANN to learn user profile

(ii) The CBR-KNN based recommender system is aninteractive recommender system that uses CBRmethod as a knowledge-based filter combined withKNN in the retrieving reusing and retaining phasesof CBR

On the other hand three evaluation metrics are consideredto compare CBR-ANN with ANN and CBR-KNN methodsincluding user effort accuracy error and user satisfaction

(i) user effort The system needs enough user feedbacksto generate valid recommendations while withoutgathering enough user feedbacks this may lead to apoor user model and inaccurate recommendationsThe number of user interactions with the systembefore reaching the final recommendation deter-mines the user effort metric

(ii) accuracy error The accuracy error denotes thedegree to which the recommender system matchesthe userrsquos preferences It is evaluated by the differencebetween the rating given by the system to the finalselected tour denoted as 119904final and the one of the userdenoted as 119904final The lower the error of accuracy is


the better the method is performing it is given asfollows

accuracy error = 1003816100381610038161003816119904final minus 119904final1003816100381610038161003816 (16)

(iii) user satisfactionThe user satisfaction is evaluated bydetermining to which degree the final recommendedtour is considered as valid by the user or not Therating assigned by the user to the final recommendedtour (119904final) is used as an indicator of user satisfactionIt is valued by an interval [0 1] that is 0 as notsatisfied to 1 as satisfied

user satisfaction = 119904final (17)

Moreover to evaluate the proposed method two differenttypes of evaluation methods are applied including per userand overall evaluation methods

(i) The per user evaluation method only takes intoaccount the recommendation process of a specificuser and computes evaluation metrics for a specificuser

(ii) The overall evaluation method computes averageminimum and maximum quality over all users LetUE119894 AE119894 and US119894 denote the user effort accuracyerror and user satisfaction of a specific user respec-tively Let 119899user denote the number of users considered(ie 20 in this paper) The mean minimum andmaximum values of the user effort over all users arecomputed as follows

MeanUE = 1119899user119899usersum119894=1

UE119894MinUE = min(119899user⋃

119894=1

UE119894) MaxUE = max(119899user⋃

119894=1

UE119894) (18)

The mean minimum and maximum values of theaccuracy error over all users are computed as follows

MeanAE = 1119899user119899usersum119894=1

AE119894MinAE = min(119899user⋃

119894=1

AE119894) MaxAE = max(119899user⋃

119894=1

AE119894) (19)

The mean minimum and maximum values of theuser satisfaction over all users are computed as fol-lows

MeanUS = 1119899user119899usersum119894=1

US119894MinUS = min(119899user⋃

119894=1

US119894) MaxUS = max(119899user⋃

119894=1

US119894) (20)

After determining the evaluation metrics an overall evalua-tion of the figures that emerge from a given user taken as anexample is described in the next subsections

43 Per User Evaluation of the Results For each user thesystem learns the current user model and recommends newtours accordingly (Section 431) Then the user selects one ofthe recommended tours and assigns a rating to itThe systemrevises the user model accordingly (Section 432) After 20iterations the final recommendation is presented to the user

431 Learning Current User Model and Recommending NewTours For each user during the retrieval and reusing stage ofa CBR cycle the proposed ANN takes the history of a givenuser as an input to determine the relationship between thevisited tours and their related ratingsThis gives the rationalebehind the learning process The proposed feedforwardmultilayer perceptron neural network consists of three layersincluding one input layer one hidden layer and one outputlayer Herein eleven POI types are considered Since theinputs of the proposed neural network are the number ofPOIs in the tour that are located in each POI type and the dis-tance traveled in the tour the input layer has twelve neuronsThe output layer has one neuron that evaluates the rating ofthe considered tour

The optimum number of hidden neurons depends onthe problem and is related to the complexity of the inputand output mapping the amount of noise in the data andthe amount of training data available Too few neuronslead to underfitting while too many neurons contribute tooverfitting For each user to obtain the optimal number ofneurons in the hidden layer different ANNs with differenthidden neurons number between one and twelve are trained

In order to train each of the ANNs the data set is dividedinto three subsets including training validation and test setsWe do consider four tours and their related ratings as trainingdata set one tour and its related rating as validation data setand one tour and its related rating as test data setThenMeanSquared Error (MSE) is computed for training validationand test sets MSE is given by the average squared differencebetween tour ratings computed by the ANN and assigned bythe user The training set is used to adjust the ANNrsquos weightsand biases while the validation set is used to prevent theoverfitting problem At execution it appears that the MSEon the validation set decreases during the initial phase of


Table 3 Mean Square Error of different neural networks structuresfor a given user

Number of neurons in hidden layer MSE1 00182 00223 00674 00295 00106 00187 00148 00119 004410 003211 004512 0038

the training However when the ANN begins to overfit thetraining data the validation MSE begins to rise The trainingstops at this point and the weights and biases related to theminimum validation MSE are returned The MSE on the testdata is not used during the ANN training but it is used tocompare the ANNs during and after training

For ANNs with hidden neurons of a number betweenone and twelve the network is trained and the Mean SquareError (MSE) value is computed Table 3 shows MSE valuesrelated to ANNs with different number of hidden neuronsfor user 1 According to this experiment it appears that thebest multilayer perceptron neural network should have fivehidden neurons as reflected by the MSE values for this user

For each user once the optimal number of hidden neu-rons is determined the ANN is trained using several trainingalgorithms that are used for training the backpropagationANN Table 4 shows Mean Square Error of different trainingalgorithms used to train theANN related to user 1The resultsshow that the Levenberg-Marquardt (LM) trainingmethod isthe best training method for user 1 because it has the leastMSE value The trained ANN determines the relationshipbetween the visited tours and their respective recommenda-tion ratings and thus models the userrsquos preferences

Similar approaches are commonly used to determinethe best number of hidden neurons and the best trainingalgorithm for the other users Table 5 shows the best numberof hidden neurons and the best training algorithm of ANNto learn the current user model The results show that GDXand LM are most often the best training functions

432 Revising the User Model Based on His Feedback Foreach user after training the ANN the new ratings of the toursare computed using the trained ANN Therefore accordingto the current user model new 119899show best rated tours arerecommended to the user

In the revision stage of CBR cycle the user reviews therecommendations and selects and rates a tour as a feedbackIn the retaining stage of CBR cycle the system revises the usermodel based on the userrsquos feedback The system computesthe property-based semantic similarity between the selected

tour and all other tours determines the 119899similar most similartours to the selected tour and assigns their ratings accordingto the selected tour rating These values are used to adaptthe user model for the next cycle by updating the weightsof the proposed ANN Therefore the system learns from thecurrent userrsquos feedback and uses it to revise the user modelThis process is iterated for 20 cycles in order to determine thefinal recommendation

433 Experimental Results for One User Figures 5 and 6show the result of the proposedmethod for user 1 considering119899show = 4 and 119899similar = 2 One can remark that the finalselected tour is different from the tours which the user haspreviously rated Similar approaches are used for the otherusers

Figure 7 shows the rating that user 1 assigns to the selectedtour at each cycle In the first cycle the proposed ANNrecommends four tours and the user selects one of themThe userrsquos rating of the first selected tour is 050 In the nextcycles the user selects one of the recommended tours and thesystem revises the recommendations according to the userfeedbacks The userrsquos rating of the selected tour reaches 080after eleven cycles

To determine the best recommendation the parametersof the proposed method should be set

434 Setting the Proposed Method Parameters To evaluatethe impact of the proposedmethod parameters the proposedsystem is tried using different 119899show and 119899similar values Table 6shows the number of iteration steps to reach a stable stateof final selected tour for different 119899show values The resultsshow that increasing 119899show reduces the number of requirediterations to reach the final selected tour

Table 7 shows the number of iteration steps to reach astable state of the final selected tour for different119899similar valuesThe results show that increasing 119899similar reduces the rating ofthe final selected tour

435 One User Result Evaluation Table 8 shows the evalu-ation results of the proposed method for user 1 The experi-ments show that the userrsquos rating of the selected tour reachesa good value of 080 after 11 cycles 05 in one cycle and070 after 12 cycles in the CBR ANN ANN and CBR KNNmethods respectively This indicates that CBR ANN needsrelatively less user effort compared to CBR KNN Theexperimental results show accuracy error values of theCBR ANN ANN and CBR KNN methods are 001 006and 004 respectively this outlines a better performance ofthe CBR ANN method regarding the accuracy error metricApplying the CBR-ANN method increases the userrsquos ratingof the selected tour through the cycles by 60 and 14relative to the ANN and CBR-ANN methods respectivelythis denotes an increasing user satisfaction

44 Overall Evaluation of the Results Table 9 shows theoverall evaluation results of the proposed method Theresults show that the CBR ANN method needs less usereffort than the CBR-KNN method and decreases the mean


Table 4 Mean Square Error of different training algorithms for a given user

Training algorithm GDX RP CGF CGP CGH SCG BFG OSS LMMSE 0033 0033 0033 0023 0013 0013 0012 0012 0010

(a) (b)

(c)

Figure 5 Proposed tour recommendation (a) the first recommended tours and (b c) usersrsquo selected tour from the first recommended toursfor a given user


(a) (b)

(c)

Figure 6 Proposed tour recommendation (a) final recommended tours and (b c) usersrsquo selected tour from final recommended tours for agiven user

minimum and maximum values of the usersrsquo efforts (ie17 33 and 13 resp) Moreover the results show thattheCBR-ANNmethodoutperforms theANNandCBR ANNregarding the overall accuracy error and user satisfaction

metrics Applying the CBR-ANNmethod increases themeanminimum and maximum values of usersrsquo satisfaction ofthe selected tour by 75 140 and 23 compared tothe ANN method respectively It also increases the mean


Table 5 Best number of hidden neurons and training algorithm ofANN to learn the user model

User number Best number of hiddenneurons Best training function

1 5 LM2 8 GDX3 3 LM4 5 CGB5 2 CGB6 4 BFG7 5 GDX8 9 RP9 8 GDX10 10 CGF11 5 CGP12 7 GDX13 7 CGF14 4 SCG15 8 RP16 3 LM17 6 SCG18 4 LM19 11 OSS20 10 RP

Table 6 Rating of the final selected tour and the number of iterationsteps to reach the final selected tour considering different 119899showvalues

119899show 2 4 10Final selected tour rating 080 080 080Number of iterations to reach the finalselected tour 19 11 5

Table 7 Rating of final selected tour and the number of iterationsteps to reach the final selected tour considering different 119899similarvalues

119899similar 1 2 5Final selected tour rating 080 080 075Number of iterations to reach final selected tour 17 11 8

Table 8 Per user evaluation of the proposed method for a givenuser

Method UE AE USCBR ANN 11 001 080ANN 1 006 050CBR KNN 12 004 070

minimum and maximum values of usersrsquo satisfaction of theselected tour by 14 33 and 14 compared to the ANN-KNNmethod respectivelyMoreover the CBR-ANNmethod

Table 9 Overall evaluation of the proposed method

Method CBR ANN ANN CBR KNNOverall UE

MeanUE 10 1 12MinUE 6 1 9MaxUE 13 1 15

Overall AEMeanAE 002 010 008MinAE 001 001 001MaxAE 007 013 009

Overall USMeanUS 070 040 060MinUS 060 025 045MaxUS 080 065 070

2 4 6 8 10 12 14 16 18 200

Iteration

045

05

055

06

065

07

075

08

Ratin

g

Figure 7 The rating that a given user assigns to the selected tour ateach cycle

has smaller mean and minimum values of accuracy errorrelative to the ANN and CBR KNNmethods This outlines abetter performance of the CBR ANN method regarding theaccuracy error metric

45 Discussion The proposed tourism recommender systemhas the following abilities

(i) It considers contextual situations in the recommenda-tion process including POIs locations POIs openingand closing times the tours already visited by the userand the distance traveled by the user Moreover itconsiders the rating assigned to the visited tours bythe user and userrsquos feedbacks as user preferences andfeedbacks

(ii) It takes into account both POIs selection and routingbetween them simultaneously to recommend themost appropriate tours

(iii) It proposes tours to a userwhohas some specific inter-ests It takes into account only his own preferences


and does not need any information about preferencesand feedbacks of the other users

(iv) It is not limited to recommending tours that havebeen previously rated by users It computes the ratingsof unseen tours that the user has not expressed anyopinion about

(v) The proposed method takes into account the userrsquosfeedbacks in an interactive framework It needs userrsquosfeedbacks to apply an interactive learning algorithmand recommend a better tour gradually Thereforeit overcomes the mentioned cold start problem Theproposed method only needs a small number of rat-ings to produce sufficiently good recommendationsMoreover it proposes some tours to a user with lim-ited knowledge about his needs Thanks to the avail-able knowledge of the historical cases less knowledgeinput from the user is required to come up with asolution Also it takes into account the changing ofthe userrsquos preferences during the recommendationprocess

(vi) The proposed method uses an implicit strategy tomodel the user by asking him to assign a rating tothe selected tour Such process requires less user effortrelative to when a user assigns a preference value toeach tour selection criteria

(vii) The proposed method recalculates user predictedratings after he has rated a single tour in a sequentialmanner In comparison with approaches in which auser rates several tours before readjusting the modelthe user sees the system output immediately Also it ispossible to react to the user provided data and adjustthem immediately

(viii) The proposed method combines CBR and ANN andexploits the advantages of each technique to compen-sate for their respective drawbacks CBR and ANNcan be directly applied to the user modeling as a reg-ression or classification problem without more trans-formation to learn the user profile Moreover a note-worthy property of the CBR is that it provides asolution for new cases by taking into account pastcases Also the trained ANN calculates the set offeature weights those playing a core role in connect-ing both learning strategies The proposed methodhas the advantages of being adaptable with respectto dynamic situations As the ANN revises the usermodel it has an online learning property and contin-uously updates the case base with new data

(ix) The results show that the proposed CBR-ANNmethod outperforms ANN based single-shot andCBR KNN based interactive recommender systemsin terms of user effort accuracy error and user satis-faction metrics

5 Conclusion and Future Works

The research developed in this paper introduces a hybridinteractive context-aware recommender system applied to

the tourism domain A peculiarity of the approach is that itcombines CBR (as a knowledge-based recommender system)with ANN (as a content-based recommender system) toovercome the mentioned cold start problem for a new userwith little prior ratings The proposed method can suggest atour to a user with limited knowledge about his preferencesand takes into account the userrsquos preference changing duringrecommendation process Moreover it considers only thegiven userrsquos preferences and feedbacks to select POIs and theroute between them on the street network simultaneously

Regarding further works additional contextual informa-tion such as budget weather user mood crowdedness andtransportation mode can be considered Currently the userhas to explicitly rate the tours thus providing feedbacks whichcan be considered as a cumbersome task One directionremaining to explore is a one where the system can obtainthe user feedbacks by analyzing the user activity such assaving or bookmarking recommended tour Moreover othertechniques in content-based filtering (ie fuzzy logic) andknowledge-based filtering (ie critiquing) can be used Alsocollaborative filtering can be combined to the proposedmethod

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this article

References

[1] C C Aggarwal Recommender Systems The Textbook Springer2016

[2] J Bobadilla F Ortega A Hernando andA Gutierrez ldquoRecom-mender systems surveyrdquo Knowledge-Based Systems vol 46 pp109ndash132 2013

[3] F Ricci B Shapira and L Rokach Recommender SystemsHandbook 2nd edition 2015

[4] J Borras A Moreno and A Valls ldquoIntelligent tourism recom-mender systems a surveyrdquo Expert Systems with Applicationsvol 41 no 16 pp 7370ndash7389 2014

[5] D Gavalas V Kasapakis C Konstantopoulos K Mastakasand G Pantziou ldquoA survey on mobile tourism RecommenderSystemsrdquo in Proceedings of the 3rd International Conference onCommunications and Information Technology ICCIT 2013 pp131ndash135 June 2013

[6] W-J Li QDong andY Fu ldquoInvestigating the temporal effect ofuser preferences with application in movie recommendationrdquoMobile Information Systems vol 2017 Article ID 8940709 10pages 2017

[7] K Zhu X He B Xiang L Zhang and A Pattavina ldquoHowdangerous are your Smartphones App usage recommendationwith privacy preservingrdquoMobile Information Systems vol 2016Article ID 6804379 10 pages 2016

[8] K Zhu Z Liu L Zhang and X Gu ldquoA mobile applicationrecommendation framework by exploiting personal preferencewith constraintsrdquoMobile Information Systems vol 2017 ArticleID 4542326 9 pages 2017

[9] M-H Kuo L-C Chen and C-W Liang ldquoBuilding andevaluating a location-based service recommendation system


with a preference adjustment mechanismrdquo Expert Systems withApplications vol 36 no 2 pp 3543ndash3554 2009

[10] GAdomavicius andA Tuzhilin ldquoContext-aware recommendersystemsrdquo in Recommender Systems Handbook pp 217ndash253Springer 2011

[11] L Baltrunas B Ludwig S Peer and F Ricci ldquoContext relevanceassessment and exploitation in mobile recommender systemsrdquoPersonal and Ubiquitous Computing vol 16 no 5 pp 507ndash5262012

[12] H Huang and G Gartner ldquoUsing context-aware collabora-tive filtering for POI recommendations in mobile guidesrdquo inAdvances in Location-Based Services Lecture Notes in Geoin-formation and Cartography pp 131ndash147 2012

[13] N S Savage M Baranski N E Chavez and T Hollerer ldquoIrsquomfeeling LoCo a location based context aware recommenda-tion systemrdquo in Advances in Location-Based Services LectureNotes in Geoinformation and Cartography pp 37ndash54 SpringerBerlin Germany 2012

[14] G D Abowd C G Atkeson J Hong S Long R Kooper andM Pinkerton ldquoCyberguide amobile context-aware tour guiderdquoWireless Networks vol 3 no 5 pp 421ndash433 1997

[15] M J Barranco J M Noguera J Castro and L Martınez ldquoAcontext-aware mobile recommender system based on locationand trajectoryrdquo in Management Intelligent Systems vol 171 ofAdvances in Intelligent Systems and Computing pp 153ndash162Springer Berlin Germany 2012

[16] V Bellotti B Begole E H Chi et al ldquoActivity-based serendip-itous recommendations with the magitti mobile leisure guiderdquoin Proceedings of the SIGCHI Conference on Human Factors inComputing Systems (CHI rsquo08) pp 1157ndash1166 ACM FlorenceItaly April 2008

[17] I R Brilhante J A Macedo F M Nardini R Perego andC Renso ldquoOn planning sightseeing tours with TripBuilderrdquoInformation Processing and Management vol 51 no 2 pp 1ndash152015

[18] I Cenamor T de la Rosa S Nunez and D Borrajo ldquoPlanningfor tourism routes using social networksrdquo Expert Systems withApplications vol 69 pp 1ndash9 2017

[19] R Colomo-Palacios F J Garcıa-Penalvo V Stantchev and SMisra ldquoTowards a social and context-aware mobile recommen-dation system for tourismrdquo Pervasive and Mobile Computingvol 38 pp 505ndash515 2017

[20] D Gavalas and M Kenteris ldquoA web-based pervasive recom-mendation system for mobile tourist guidesrdquo Personal andUbiquitous Computing vol 15 no 7 pp 759ndash770 2011

[21] J He H Liu and H Xiong ldquoSocoTraveler Travel-packagerecommendations leveraging social influence of different rela-tionship typesrdquo Information andManagement vol 53 no 8 pp934ndash950 2016

[22] T Horozov N Narasimhan and V Vasudevan ldquoUsing loca-tion for personalized POI recommendations in mobile envi-ronmentsrdquo in Proceedings of the International Symposium onApplications and the Internet (SAINT rsquo06) pp 124ndash129 IEEEPhoenix Ariz USA January 2006

[23] M Kenteris D Gavalas and A Mpitziopoulos ldquoA mobiletourism recommender systemrdquo in Proceedings of the 15th IEEESymposium on Computers and Communications (ISCC rsquo10) pp840ndash845 IEEE Riccione Italy June 2010

[24] J A Mocholi J Jaen K Krynicki A Catala A Picon and ACadenas ldquoLearning semantically-annotated routes for context-aware recommendations on map navigation systemsrdquo AppliedSoft Computing Journal vol 12 no 9 pp 3088ndash3098 2012

[25] AMoreno AValls D Isern LMarin and J Borras ldquoSigTurE-Destination ontology-based personalized recommendation ofTourism and Leisure Activitiesrdquo Engineering Applications ofArtificial Intelligence vol 26 no 1 pp 633ndash651 2013

[26] Z Xu L Chen and G Chen ldquoTopic based context-awaretravel recommendation method exploiting geotagged photosrdquoNeurocomputing vol 155 pp 99ndash107 2015

[27] W-S Yang and S-Y Hwang ldquoITravel a recommender systemin mobile peer-to-peer environmentrdquo Journal of Systems andSoftware vol 86 no 1 pp 12ndash20 2013

[28] B Xu Z Ding and H Chen ldquoRecommending locations basedon usersrsquo periodic behaviorsrdquo Mobile Information Systems vol2017 Article ID 7871502 9 pages 2017

[29] Y Guo J Hu and Y Peng ldquoResearch of new strategies forimproving CBR systemrdquo Artificial Intelligence Review vol 42no 1 pp 1ndash20 2014

[30] M M Richter ldquoThe search for knowledge contexts andCase-Based Reasoningrdquo Engineering Applications of ArtificialIntelligence vol 22 no 1 pp 3ndash9 2009

[31] C K Riesbeck and R C Schank Inside Case-Based ReasoningPsychology Press 2013

[32] L Chen G Chen and F Wang ldquoRecommender systems basedon user reviews the state of the artrdquo User Modeling and User-Adapted Interaction vol 25 no 2 pp 99ndash154 2015

[33] F Ricci D Cavada N Mirzadeh and A Venturini ldquoCase-based travel recommendationsrdquo Destination RecommendationSystems Behavioural Foundations and Applications pp 67ndash932006

[34] C-SWang andH-L Yang ldquoA recommendermechanism basedon case-based reasoningrdquo Expert Systems with Applications vol39 no 4 pp 4335ndash4343 2012

[35] D T LaroseDiscovering Knowledge in Data An Introduction toData Mining John Wiley amp Sons 2014

[36] I N da Silva D H Spatti R A Flauzino L H B Liboni and SF dos Reis AlvesArtificial Neural Networks A Practical CourseSpringer 2016

[37] J Heaton Introduction to the Math of Neural Networks HeatonResearch Inc 2012

[38] D Gavalas C Konstantopoulos K Mastakas and G PantziouldquoMobile recommender systems in tourismrdquo Journal of Networkand Computer Applications vol 39 no 1 pp 319ndash333 2014

[39] R P Biuk-Aghai S Fong and Y-W Si ldquoDesign of a rec-ommender system for mobile tourism multimedia selectionrdquoin Proceedings of the 2nd International Conference on InternetMultimedia Services Architecture and Applications (IMSAA rsquo08)pp 1ndash6 IEEE Bangalore India December 2008

[40] F Martinez-Pabon J C Ospina-Quintero G Ramirez-Gonzalez and M Munoz-Organero ldquoRecommending adsfrom trustworthy relationships in pervasive environmentsrdquoMobile Information Systems vol 2016 Article ID 8593173 18pages 2016

[41] T Berka andM Ploszlignig ldquoDesigning recommender systems fortourismrdquo in Proceedings of the 11th International Conference onInformation Technology in Travel amp Tourism (ENTER rsquo04) 2004

[42] A Hinze and S Junmanee ldquoTravel recommendations in amobile tourist information systemrdquo in ISTArsquo 2005 4th Interna-tional Conference Lecture Notes in Informatics pp 89ndash100 GIedition 2005

[43] W Husain and L Y Dih ldquoA framework of a PersonalizedLocation-based traveler recommendation system in mobileapplicationrdquo International Journal of Multimedia and Ubiqui-tous Engineering vol 7 no 3 pp 11ndash18 2012


[44] L McGinty and B Smyth ldquoComparison-based recommen-dationrdquo in ECCBR 2002 Advances in Case-Based Reason-ing Lecture Notes in Computer Science (including subseriesLecture Notes in Artificial Intelligence and Lecture Notes inBioinformatics) pp 575ndash589 2002

[45] H Shimazu ldquoExpertClerkNavigating shoppersrsquo buying processwith the combination of asking and proposingrdquo in Proceedingsof the 17th International Joint Conference onArtificial Intelligence(IJCAI rsquo01) pp 1443ndash1448 August 2001

[46] H Shimazu ldquoExpertClerk A conversational case-based rea-soning tool for developing salesclerk agents in e-commercewebshopsrdquo Artificial Intelligence Review vol 18 no 3-4 pp223ndash244 2002

[47] R Burke ldquoHybrid recommender systems survey and experi-mentsrdquoUserModelling andUser-Adapted Interaction vol 12 no4 pp 331ndash370 2002

[48] A K Dey ldquoUnderstanding and using contextrdquo Personal andUbiquitous Computing vol 5 no 1 pp 4ndash7 2001

[49] P M P Rosa J J P C Rodrigues and F Basso ldquoA weight-awarerecommendation algorithm for mobile multimedia systemsrdquoMobile Information Systems vol 9 no 2 pp 139ndash155 2013

[50] H Ince ldquoShort term stock selection with case-based reasoningtechniquerdquoApplied SoftComputing Journal vol 22 pp 205ndash2122014

[51] I Pereira and A Madureira ldquoSelf-Optimization module forScheduling using Case-based ReasoningrdquoApplied Soft Comput-ing Journal vol 13 no 3 pp 1419ndash1432 2013

[52] R BergmannK-DAlthoff S Breen et alDeveloping IndustrialCase-Based Reasoning Applications The INRECA MethodologySpringer 2003

[53] A Aamodt and E Plaza ldquoCase-based reasoning foundationalissues methodological variations and system approachesrdquo AICommunications vol 7 no 1 pp 39ndash59 1994

[54] S Likavec and F Cena ldquoProperty-based semantic similaritywhat countsrdquo in CEUR Workshop Proceedings pp 116ndash1242015

[55] A Tversky ldquoFeatures of similarityrdquoPsychological Review vol 84no 4 pp 327ndash352 1977

[56] K Christakopoulou F Radlinski and K Hofmann ldquoTowardsconversational recommender systemsrdquo in Proceedings of the22nd ACM SIGKDD International Conference on KnowledgeDiscovery and Data Mining KDD 2016 pp 815ndash824 SanFrancisco Calif USA August 2016

[57] B Kveton and S Berkovsky ldquoMinimal interaction search inrecommender systemsrdquo in Proceedings of the 20th ACM Inter-national Conference on Intelligent User Interfaces (IUI rsquo15) pp236ndash246 ACM Atlanta Ga USA April 2015

[58] T Mahmood G Mujtaba and A Venturini ldquoDynamic person-alization in conversational recommender systemsrdquo InformationSystems and e-Business Management vol 12 no 2 pp 213ndash2382014

[59] T Mahmood and F Ricci ldquoImproving recommender systemswith adaptive conversational strategiesrdquo in Proceedings of the20th ACM Conference on Hypertext and Hypermedia (HT rsquo09)pp 73ndash82 ACM Turin Italy July 2009

[60] E R Ciurana Simo Development of a Tourism RecommenderSystem 2012

[61] C-C Yu and H-P Chang ldquoPersonalized location-based rec-ommendation services for tour planning in mobile tourismapplicationsrdquo in Proceedings of the 10th International Conferenceon E-Commerce andWeb Technologies (EC-Web rsquo09) pp 38ndash49Springer Linz Austria 2009

Submit your manuscripts athttpswwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Distributed Sensor Networks


Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014


ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014


Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Electrical and Computer Engineering

Journal of

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of



Computational Intelligence and Neuroscience

Industrial EngineeringJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in



these recommendations not only to the userrsquos preferencesbut also to the contextual situation to generate more relevantsuggestions

However and to the best of our knowledge previouscontext-aware tourism recommender systems developed sofar have not completely addressed these issues [9 11ndash28] Oneweakness of these researches is that they often suggest to agiven user a list of POIs which are specific tourist attractionsthat a user may find interesting such as a historical or culturalbuildings or any places worth being visited rather than atour as an ordered set of POIs Moreover and despite thefact that collaborative recommendations are often providedbased on other users self-learning processes based on thecontext and assessment of a given user are not taken intoaccount Also another common drawback of related context-aware tourism recommender systems is that they generallyoffer suggestions to a given user in one single session onlyIndeed most of the users rarely know all their needs atfirst Therefore initial recommendations are likely to be notalways satisfactory Moreover most current context-awaretourism recommender systems often suffer from the coldstart problem which occurs when a new user with little priorratings has been registered to the system making it difficultto suggest meaningful recommendations for that new userThey also assume that the userrsquos preferences are stable and donot take into account userrsquos preferences variations over time

In order to overcome these problems several researchersapply case based reasoning (CBR) [29ndash31] to develop casebased recommender systems that present userrsquos preferenceas a query item that he likes and each item case as a setof attributes [32ndash34] These systems retrieve items that aresimilar to the userrsquos query case The main advantages of CBRis that (a) it can be directly applied to learn the user profileat the user modeling level (b) it has the ability to learn thedynamic behavior of the system over time and (c) it canprovide effective explanation mechanisms when a sufficientnumber of previous cases are available

However an important issue still left for CBR is howto predict toursrsquo ratings in order to recommend the mosttop rated tours to a user In other words some features aremore important than the others and then appropriate featureweighting should be executed before any rating prediction inorder to take into account each feature relative importanceIn existing case based recommender systems the 119896-nearestneighbor (KNN) method [35] is widely used in ratingprediction [32 33] but it assumes that all selected features areequally important this being not true in many cases as forinstance each POI type is likely to have a specific interestvalue Another open problem is how to progressively adaptthe user model based on the information derived from thedifference identified between a selected tour and the othertours in order to suggest a tour in a next cycle

These problems lead us to proposing a hybrid methodthat combines an artificial neural network (ANN) [36 37]with CBR Our research develops a cold start context-awarerecommender system applied to the tourism domain thatoffers personalized tours to the user based on his preferencesand some contextual information Our objective is to over-come the above-mentioned problems by considering userrsquos

feedback and applyingCBRusing anANNThe contributionsof this paper include (1) providing a tourism recommendersystem that considers contextual situations in the recommen-dation process (2) proposing an interactive framework thatconsiders userrsquos feedback to (a) suggest tours to a user withlimited knowledge about his own needs (b) overcome thecold start problem which occurs when a new user with littleprior ratings uses the system and (c) take into account userrsquospreferences variations (3) recommending some tours to theusers including both POIs and routes between them over thestreet network and (4) proposing tours to the user takinginto account only his own preferences and feedbacks withoutconsidering the preferences and feedbacks of other users

The rest of this paper is as follows Section 2 introducesseveral concepts from recommender to context-aware rec-ommender systems It also briefly reviews context-awarerecommender systems applied to the tourism domain Thenit presents the principles behind CBR and ANN and a briefintroduction to semantic similarity principles The proposedmethod and its implementation results are explained inSections 3 and 4 respectively Finally Section 5 providesconclusions and outlines future works

2 Literature Review

This section describes several basic concepts on recom-mender and context-aware recommender systems with aspecific focus on the tourism domain The main principlesbehind CBR ANN and property-based semantic similarityare briefly discussed

21 Recommender Systems Recommender systems (RSs)select some items proven to better match userrsquos preferencesand reduce information overload [4 5 38] Without lossof generality let us categorize current recommendationapproaches towards content-based recommender systemscollaborative recommender systems knowledge-based rec-ommender systems and hybrid recommender systems Theyare discussed below

Content-based recommender systems derive the similarityof features with respect to userrsquos preferences and select theones that best match userrsquos preferences [39] Collaborativerecommender systems make recommendations based on thepreferences of similar users [3 40] Knowledge-based recom-mender systems apply reasoning mechanisms to make appro-priate suggestions according to userrsquos preferences as relatedto items properties [41ndash43] An interactive recommendersystem can be also considered as a knowledge-based rec-ommender system Navigation-by-proposing is one formof interactive recommender systems that are based onpreference-based feedback [44ndash46]Hybrid recommender sys-tems apply a combination of current recommendation tech-niques The main idea behind hybrid recommender systemsis to exploit the advantages of one technique to compensatefor the shortcomings of another and improve the overallperformance [47] A metalevel hybrid recommender systemis a class of hybrid recommender system that uses the modellearned by one recommender system as an input for another[47]













Table1Con

text-awarer

ecom

mendersystemsa

ppliedto

thetou

rism

domain

Recommender

syste

m

Con

text-awarep

roperties

RecommenderS

ystem

prop

ertie

s

Usedcontext

Con

text-aware

recommendatio

nRe

commendedIte

mRe

commendatio

ntechniqu

eUser

feedback

User

Environm

ent

Locatio

nTime

Con

textual

prefilterin

gCon

textual

postfi

lterin

gCon

textual

mod

eling

POI

Hotel

Restaurant

Leisu

reRo

ute

Tour

Con

tent-

based

Collabo

rativ

eKn

owledge-

based

Hybrid

Yes

No

Cyberguide

[14]

GeoWhiz[22]

Magitti[16]

LBSR

M[9]

MTR

S[20]

REJA

[15]

Irsquomfeeling

Loco

[13]

ReRe

x[11]

iTravel[27]

SigTurE-

Destin

ation

[25]

TRIPBU

ILDER

[17]

[26]

SocoTravele

r[21]

POST

-VIA

360

[19]

PlanTo

ur[18

]

[28]


New case

New case

Problem

Learnedcase

Solvedcased

Revisedcase

Reuse

Revise

Reta

inRetrieved

case

Precedentcases

Generalknowledge

Retrieve





x1

x2

xnＣＨＪＯＮ

a3w2

a2w1

a1



119895=1

119908119897 (119894 119895) 119886119897 (119895) + 119887119897 (119894)) (2)



119908119896 = 119908119896minus1 + Δ119908119896 (3)







GDX


Δ119908119896 = 119901Δ119908119896minus1 + 120572119901Δ119864119896Δ119908119896

RP


CGF




120573119896 = 119892119879119896 119892119896119892119879119896minus1119892119896minus1

CGP






SCG



BFG




OSS


119866(119896+1) = 119866(119896) + 119901119901119879119901119879V minus(119866(119896)V) V119879119866(119896)

V119879119866(119896)V + (V119879119866(119896)V) 119906119906119879where 119901 = 119908(119896+1) minus 119908(119896)

V = 119892(119896+1) minus 119892(119896)119906 = 119901119901119879V minus 119866(119896)V


LM




SIM (1198741 1198742) =119899119901sum119902=1

119908119902SIM119901119902

SIM119901119902 = 120572CF119901119902 (1198741 1198742)120573CF119901119902 (1198741) + 120574CF119901119902 (1198742) + 120572CF119901119902 (1198741 1198742) (4)

















Tour119894 = tr1198941 tr1198942 tr119894119899 (8)






Tour119894 (10)







Dist (tr119894119895 tr119894(119895+1)) (13)
















119908119902SIM119901119902 SIM119901119902



(14)






Userrsquos feedback

Time context

Environmentcontext

POIs context

Streets context

Start time

End time

Visited tour

Tour rating

MLP ANN




Retrieve and reuse

Revise

Retain

Stoppingcriteria

Final proposed tour

Yes



No


nＭＢＩＱ

nＭＣＧＣＦＬ











tm119894(119895+1) = tm119894119895 + 2 hours (15)

























119894=1


119894=1

UE119894) (18)




119894=1


119894=1

AE119894) (19)




119894=1


119894=1

US119894) (20)


























(a) (b)

(c)



(a) (b)

(c)




















2 4 6 8 10 12 14 16 18 200

Iteration

045

05

055

06

065

07

075

08

Ratin

g





















References








































































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in















Table1Con

text-awarer

ecom

mendersystemsa

ppliedto

thetou

rism

domain

Recommender

syste

m

Con

text-awarep

roperties

RecommenderS

ystem

prop

ertie

s

Usedcontext

Con

text-aware

recommendatio

nRe

commendedIte

mRe

commendatio

ntechniqu

eUser

feedback

User

Environm

ent

Locatio

nTime

Con

textual

prefilterin

gCon

textual

postfi

lterin

gCon

textual

mod

eling

POI

Hotel

Restaurant

Leisu

reRo

ute

Tour

Con

tent-

based

Collabo

rativ

eKn

owledge-

based

Hybrid

Yes

No

Cyberguide

[14]

GeoWhiz[22]

Magitti[16]

LBSR

M[9]

MTR

S[20]

REJA

[15]

Irsquomfeeling

Loco

[13]

ReRe

x[11]

iTravel[27]

SigTurE-

Destin

ation

[25]

TRIPBU

ILDER

[17]

[26]

SocoTravele

r[21]

POST

-VIA

360

[19]

PlanTo

ur[18

]

[28]


New case

New case

Problem

Learnedcase

Solvedcased

Revisedcase

Reuse

Revise

Reta

inRetrieved

case

Precedentcases

Generalknowledge

Retrieve





x1

x2

xnＣＨＪＯＮ

a3w2

a2w1

a1



119895=1

119908119897 (119894 119895) 119886119897 (119895) + 119887119897 (119894)) (2)



119908119896 = 119908119896minus1 + Δ119908119896 (3)







GDX


Δ119908119896 = 119901Δ119908119896minus1 + 120572119901Δ119864119896Δ119908119896

RP


CGF




120573119896 = 119892119879119896 119892119896119892119879119896minus1119892119896minus1

CGP






SCG



BFG




OSS


119866(119896+1) = 119866(119896) + 119901119901119879119901119879V minus(119866(119896)V) V119879119866(119896)

V119879119866(119896)V + (V119879119866(119896)V) 119906119906119879where 119901 = 119908(119896+1) minus 119908(119896)

V = 119892(119896+1) minus 119892(119896)119906 = 119901119901119879V minus 119866(119896)V


LM




SIM (1198741 1198742) =119899119901sum119902=1

119908119902SIM119901119902

SIM119901119902 = 120572CF119901119902 (1198741 1198742)120573CF119901119902 (1198741) + 120574CF119901119902 (1198742) + 120572CF119901119902 (1198741 1198742) (4)

















Tour119894 = tr1198941 tr1198942 tr119894119899 (8)






Tour119894 (10)







Dist (tr119894119895 tr119894(119895+1)) (13)
















119908119902SIM119901119902 SIM119901119902



(14)






Userrsquos feedback

Time context

Environmentcontext

POIs context

Streets context

Start time

End time

Visited tour

Tour rating

MLP ANN




Retrieve and reuse

Revise

Retain

Stoppingcriteria

Final proposed tour

Yes



No


nＭＢＩＱ

nＭＣＧＣＦＬ











tm119894(119895+1) = tm119894119895 + 2 hours (15)

























119894=1


119894=1

UE119894) (18)




119894=1


119894=1

AE119894) (19)




119894=1


119894=1

US119894) (20)


























(a) (b)

(c)



(a) (b)

(c)




















2 4 6 8 10 12 14 16 18 200

Iteration

045

05

055

06

065

07

075

08

Ratin

g





















References








































































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in




New case

New case

Problem

Learnedcase

Solvedcased

Revisedcase

Reuse

Revise

Reta

inRetrieved

case

Precedentcases

Generalknowledge

Retrieve





x1

x2

xnＣＨＪＯＮ

a3w2

a2w1

a1



119895=1

119908119897 (119894 119895) 119886119897 (119895) + 119887119897 (119894)) (2)



119908119896 = 119908119896minus1 + Δ119908119896 (3)







GDX


Δ119908119896 = 119901Δ119908119896minus1 + 120572119901Δ119864119896Δ119908119896

RP


CGF




120573119896 = 119892119879119896 119892119896119892119879119896minus1119892119896minus1

CGP






SCG



BFG




OSS


119866(119896+1) = 119866(119896) + 119901119901119879119901119879V minus(119866(119896)V) V119879119866(119896)

V119879119866(119896)V + (V119879119866(119896)V) 119906119906119879where 119901 = 119908(119896+1) minus 119908(119896)

V = 119892(119896+1) minus 119892(119896)119906 = 119901119901119879V minus 119866(119896)V


LM




SIM (1198741 1198742) =119899119901sum119902=1

119908119902SIM119901119902

SIM119901119902 = 120572CF119901119902 (1198741 1198742)120573CF119901119902 (1198741) + 120574CF119901119902 (1198742) + 120572CF119901119902 (1198741 1198742) (4)

















Tour119894 = tr1198941 tr1198942 tr119894119899 (8)






Tour119894 (10)







Dist (tr119894119895 tr119894(119895+1)) (13)
















119908119902SIM119901119902 SIM119901119902



(14)






Userrsquos feedback

Time context

Environmentcontext

POIs context

Streets context

Start time

End time

Visited tour

Tour rating

MLP ANN




Retrieve and reuse

Revise

Retain

Stoppingcriteria

Final proposed tour

Yes



No


nＭＢＩＱ

nＭＣＧＣＦＬ











tm119894(119895+1) = tm119894119895 + 2 hours (15)

























119894=1


119894=1

UE119894) (18)




119894=1


119894=1

AE119894) (19)




119894=1


119894=1

US119894) (20)


























(a) (b)

(c)



(a) (b)

(c)




















2 4 6 8 10 12 14 16 18 200

Iteration

045

05

055

06

065

07

075

08

Ratin

g





















References








































































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in






GDX


Δ119908119896 = 119901Δ119908119896minus1 + 120572119901Δ119864119896Δ119908119896

RP


CGF




120573119896 = 119892119879119896 119892119896119892119879119896minus1119892119896minus1

CGP






SCG



BFG




OSS


119866(119896+1) = 119866(119896) + 119901119901119879119901119879V minus(119866(119896)V) V119879119866(119896)

V119879119866(119896)V + (V119879119866(119896)V) 119906119906119879where 119901 = 119908(119896+1) minus 119908(119896)

V = 119892(119896+1) minus 119892(119896)119906 = 119901119901119879V minus 119866(119896)V


LM




SIM (1198741 1198742) =119899119901sum119902=1

119908119902SIM119901119902

SIM119901119902 = 120572CF119901119902 (1198741 1198742)120573CF119901119902 (1198741) + 120574CF119901119902 (1198742) + 120572CF119901119902 (1198741 1198742) (4)

















Tour119894 = tr1198941 tr1198942 tr119894119899 (8)






Tour119894 (10)







Dist (tr119894119895 tr119894(119895+1)) (13)
















119908119902SIM119901119902 SIM119901119902



(14)






Userrsquos feedback

Time context

Environmentcontext

POIs context

Streets context

Start time

End time

Visited tour

Tour rating

MLP ANN




Retrieve and reuse

Revise

Retain

Stoppingcriteria

Final proposed tour

Yes



No


nＭＢＩＱ

nＭＣＧＣＦＬ











tm119894(119895+1) = tm119894119895 + 2 hours (15)

























119894=1


119894=1

UE119894) (18)




119894=1


119894=1

AE119894) (19)




119894=1


119894=1

US119894) (20)


























(a) (b)

(c)



(a) (b)

(c)




















2 4 6 8 10 12 14 16 18 200

Iteration

045

05

055

06

065

07

075

08

Ratin

g





















References








































































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in





SIM (1198741 1198742) =119899119901sum119902=1

119908119902SIM119901119902

SIM119901119902 = 120572CF119901119902 (1198741 1198742)120573CF119901119902 (1198741) + 120574CF119901119902 (1198742) + 120572CF119901119902 (1198741 1198742) (4)

















Tour119894 = tr1198941 tr1198942 tr119894119899 (8)






Tour119894 (10)







Dist (tr119894119895 tr119894(119895+1)) (13)
















119908119902SIM119901119902 SIM119901119902



(14)






Userrsquos feedback

Time context

Environmentcontext

POIs context

Streets context

Start time

End time

Visited tour

Tour rating

MLP ANN




Retrieve and reuse

Revise

Retain

Stoppingcriteria

Final proposed tour

Yes



No


nＭＢＩＱ

nＭＣＧＣＦＬ











tm119894(119895+1) = tm119894119895 + 2 hours (15)

























119894=1


119894=1

UE119894) (18)




119894=1


119894=1

AE119894) (19)




119894=1


119894=1

US119894) (20)


























(a) (b)

(c)



(a) (b)

(c)




















2 4 6 8 10 12 14 16 18 200

Iteration

045

05

055

06

065

07

075

08

Ratin

g





















References








































































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in

















119908119902SIM119901119902 SIM119901119902



(14)






Userrsquos feedback

Time context

Environmentcontext

POIs context

Streets context

Start time

End time

Visited tour

Tour rating

MLP ANN




Retrieve and reuse

Revise

Retain

Stoppingcriteria

Final proposed tour

Yes



No


nＭＢＩＱ

nＭＣＧＣＦＬ











tm119894(119895+1) = tm119894119895 + 2 hours (15)

























119894=1


119894=1

UE119894) (18)




119894=1


119894=1

AE119894) (19)




119894=1


119894=1

US119894) (20)


























(a) (b)

(c)



(a) (b)

(c)




















2 4 6 8 10 12 14 16 18 200

Iteration

045

05

055

06

065

07

075

08

Ratin

g





















References








































































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in





Userrsquos feedback

Time context

Environmentcontext

POIs context

Streets context

Start time

End time

Visited tour

Tour rating

MLP ANN




Retrieve and reuse

Revise

Retain

Stoppingcriteria

Final proposed tour

Yes



No


nＭＢＩＱ

nＭＣＧＣＦＬ











tm119894(119895+1) = tm119894119895 + 2 hours (15)

























119894=1


119894=1

UE119894) (18)




119894=1


119894=1

AE119894) (19)




119894=1


119894=1

US119894) (20)


























(a) (b)

(c)



(a) (b)

(c)




















2 4 6 8 10 12 14 16 18 200

Iteration

045

05

055

06

065

07

075

08

Ratin

g





















References








































































Advances in

FuzzySystems


Volume 2014












Journal of



Advances in

Multimedia


Biomedical Imaging


Advances in


RoboticsJournal of










Advances in


























119894=1


119894=1

UE119894) (18)




119894=1


119894=1

AE119894) (19)




119894=1


119894=1

US119894) (20)


























(a) (b)

(c)



(a) (b)

(c)




















2 4 6 8 10 12 14 16 18 200

Iteration

045

05

055

06

065

07

075

08

Ratin

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a cold start context-aware recommender system...

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