PITAGORA: Recommending Users and Local Experts in anAirport Social Network

Andrea Ferracani, Daniele Pezzatini, Andrea Benericetti, Marco Guiducci, Alberto Del BimboUniversità degli Studi di Firenze - MICC

Firenze, Italy[name.surname]@unifi.it

ABSTRACTIn this demo we present PITAGORA1: a mobile web contex-tual social network designed for the check-in area of an air-port. The app provides recommendation of potential friends,local experts and targeted services. Recommendation is hy-brid and combines social media analysis and collaborativefiltering techniques. Users’ recommendation has been eval-uated through a user study with good results.

Categories and Subject DescriptorsH.3.1 [Information Storage and Retrieval]: ContentAnalysis And Indexing; H.5.m [Information Storage andRetrieval]: Information Interfaces And Presentation—Mis-cellaneous

General TermsExperimentation, Contextual Social Networking, Mobile WebApplication

KeywordsSocial Media Analysis, User Profiling, Local Experts Rec-ommendation, Users Recommendation

1. MOTIVATIONS AND PREVIOUS WORKKnowledge extracted from online social networks (e.g. Face-

book and LinkedIn), combined with information about users’location, can be exploited to build more targeted contextualservices, such as recommendation of people and facilities.In this demo we present PITAGORA, a mobile social net-work which exploits analysis of social media to provide friendand local experts recommendation in the check-in area of anairport. The proposed analysis techniques and recommenda-tion approaches can be applied also in other domains wheresocial applications may be used to offer contextual services.

1Demo video available at http://bit.ly/1GgtUrN

Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full cita-tion on the first page. Copyrights for third-party components of this work must behonored. For all other uses, contact the Owner/Author(s). Copyright is held by theowner/author(s).ACM Multimedia ’15 Brisbane, AustraliaACM 978-1-4503-3459-4/15/10.DOI: http://dx.doi.org/10.1145/2733373.2807980.

In the check-in area of an airport people basically have littletime but also the opportunity to get in touch with other peo-ple. Usually, passengers are in airports for business or leisureand, in both cases, it has been demonstrated that they aremore open to social interactions than usual [1]. In this con-text, recommendation systems and interfaces can be reallyvaluable provided you take into account the motivations andneeds of users temporary co-located in the same place. Inthe case of an airport check-in area, for example, the passen-ger’s destination and the reason of the travel (professionalor not) can be essential infos to improve general-purposerecommenders.

Section 2 describes the social network and gives insightson the user interface (Section. 2.1) and the two main recom-mendation modules: friends prediction in Section 2.2 andlocal experts suggestion in Section 2.3. Recommender eval-uation is conducted in Section 3.

2. THE SOCIAL NETWORKPITAGORA has been developed as a mobile web applica-

tion available in the check-in area of an airport. The goalof the application is to improve passengers experience pro-viding an enhanced people recommender by exploiting sit-uational information. The application allows the user tosearch and display flight infos, to check the presence of pas-sengers on his same flight and to communicate with themthrough a realtime chat. Recommendations are mainly con-stituted by suggestions of other users who share the sameinterests. Users are also provided with a recommendationof local experts, present at the airport, on the basis of theirflight destination. The system uses an hybrid approach tothe recommendation problem combining content-based andcollaborative filtering. As well-known in literature hybridapproaches give better results solving issues related to thelarge amount and the sparsity of the data.

2.1 User InterfaceThe user interface is composed by two main views: 1)

a search view and a 2) browsing view. At the first accessthe user is provided with an autosuggest search input whichallows to identify his/her own flight. Once the user hasselected the flight, the interface proposes a sliding menubased panel (i.e. browsing view) organised in several sec-tions: 1) profile: it displays flight infos, allows to login withboth Facebook and LinkedIn and provides recommendationof destination-based local experts; 2) leisure: it providesrecommendation of users weighting more Facebook data; 3)professional: it provides recommendation of users weighting

more LinkedIn data; 4) chat: it allows to chat in realtime;5) search flight: it can be used to go back to the searchview. Recommendations are shown as lists of records whichbehave as an accordion: each item can be expanded to showthings in common between users: demographic info, Face-book ‘likes’ (leisure), connections, groups and companies fol-lowed (professional). Users can be ordered by similarity ororganised visually with a taxonomy of categories. Leisurerecommendations contemplate also retails in the airport se-mantically matching user interests (e.g. liquor store if theuser likes dry gin and tequila). Contextual retail suggestionscan be exploited by the user in the chat to invite other usersfor dating.

2.2 Data Analysis and Users’ Recommenda-tion

Users are profiled analysing data extracted from Facebookand LinkedIn accounts. The different nature of this datais exploited in order to obtain recommendations concern-ing leisure (Facebook) and professional aspects (LinkedIn).When the user logs into the system the following data areextracted: a) demographic infos, connections, level of edu-cation, job history (Facebook and LinkedIn); b) page ‘likes’,photo albums (Facebook) c) groups and companies followed(LinkedIn). Profiles are described as vectors of pages onwhich users have expressed a ‘like’ and groups or compa-nies followed. In order to solve the sparsity problem, aco-occurrence matrix is used to infer additional possible re-sources of interests. Users’ recommendation is then achievedwith a standard user-based algorithm considering the distri-bution of user’s interests and computing a nearest-N users’neighbourhood with the Euclidean distance. Sparsity re-duction is performed offline, using an item-based algorithm.Demographic data and professional history infos are used toadjust features’ intra-weights for recommendation. Perfor-mance of the users’ recommender is evaluated in Section 3.

2.3 Local Experts RecommendationThe goal of the local experts recommender is to suggest

users that have an high level of travel experience about theflight city or region destination. The identification of lo-cal experts is obtained through the analysis of social mediaextracted from Facebook. In particular, user travels and lo-cation history are computed analysing geo-tagged pictures:travels are identified considering photos and photo albumsmetadata, birthplaces and places of residence and exploitedto produce destination-based recommendations.The proposed method for local experts estimation buildsupon some approaches [2] where human location history isexploited to recommend points of interests in geographicalareas: count of city visits, social media analysis, user ac-tivity in the social network and cities’ correlation are usedin PITAGORA to estimate the user travel experience percity. Given a set of users U and a set of visited cities C, amatrix V is defined where the item vij of V represents howmany times the user ui has visited the city cj . Visited citiesare computed by analysing data from Facebook, as statedabove, but to reduce the sparsity problem also data fromuser’s activity in the social network is exploited: selectionsof flight destinations are considered as visits. The travel ex-perience E of users is calculated iteratively on the basis ofthe number of travels in cities as follows: En = En−1 ·V ·V T .En indicates the vector E at the n iteration that is initialised

with E0 = (1, 1, · · · , 1). The vector E is then normalised,dividing by its highest value. The correlation CORR(ci, cj)between two cities ci and cj is expressed as CORR(ci, cj) =∑

uk∈U′ α ∗ ek. U ′ represents the group of users who hasvisited both ci and cj , ek is the component of E relative tothe user uk. The weight factor α, with 0 < α ≤ 1 is definedtaking into account the Euclidean distance between latitudeand longitude of the two cities. Finally, we compute thelevel of experience of a user u for a given city c. To this end,we define nk as the number of visits of the user u in the cityck, based on previous activity on the network (i.e. previousflight destinations). In addition the number of geo-taggedphotos pk that the user has published in ck is extracted fromFacebook. On this basis a rating expu,c can be assigned to a

user for a city as expu,c =∑|C|

k=0(1 +pk)∗nk ∗CORR(c, ck).Furthermore, if a user lives (or has lived) in c or if she had ed-ucational/professional experiences in the city a bonus to therating expu,c is given. Local experts whose score is greaterthan a threshold are shown as a list of recommendation, indescending order, in a dedicated view.

3. EVALUATIONThe proposed users’ recommendation system is based on

the ranking of user similarities and can be seen as an infor-mation retrieval system, considering a user as a query term.To evaluate the relevance of the recommendations the nor-malised Cumulative Discounted Gain (nDCG) measure hasbeen used. Relevance scores are computed comparing thelist of recommended users with the ideal list given by theuser. The ground truth has been collected asking 150 usersto express a relevance score (on a 0 to 3 scale) for the firstJ people suggested by the system. In this experiment, alogarithm with base 2 is used to ensure all positions are dis-counted. The nDCG for the top-J item results in values of0.767 with j=5 and 0.872 for j=10.

4. CONCLUSIONSIn this demo we present PITAGORA, a mobile airport

social network featuring users’ recommendation and sug-gestion of local experts. Novel approaches are proposedwhich take into account profiling techniques from social me-dia analysis (e.g. Facebook and LinkedIn) and inferred rat-ings for users’ neighbourhood detection and identification.An evaluation of the effectiveness of the solutions is pro-vided through a user study based on the nDCG measurewhich shows good results.

5. ACKNOWLEDGMENTSPITAGORA - Innovative technologies and processes for Air-

port Management R&D project was supported by the RegionalGovernment of Tuscany (POR CReO - Bando Unico R&S 2012)and the European Regional Development Fund.

6. REFERENCES[1] Z. Rubin. Disclosing oneself to a stranger: Reciprocity and

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[2] Y. Zheng, L. Zhang, X. Xie, and W.-Y. Ma. Miningcorrelation between locations using human location history.In Proceedings of the 17th ACM SIGSPATIAL InternationalConference on Advances in Geographic InformationSystems, GIS ’09, pages 472–475, New York, NY, USA,2009. ACM.