techniques for context-aware and cold-start recommendations

PhD Thesis Defense, 28th Ph.D. Cycle, May 2016

Techniques for Context-Aware and Cold-Start Recommendations

Matthias BraunhoferSupervisor: Prof. Francesco Ricci

Free University of Bozen - BolzanoPiazza Domenicani 3, 39100 Bolzano, Italy

[email protected]

mailto:[email protected]


Outline

2

• Context-Aware Recommenders and the Cold-Start Problem

• State of the Art

• South Tyrol Suggests Application Scenario

• Hybrid Context-Aware Recommendation Algorithms

• Active Learning for Context-Aware Recommenders

• Conclusions and Future Work


• Recommender Systems (RSs) are information filtering and decision support tools suggesting interesting items to the user based on feedback

• Explicit feedback (e.g., ratings) vs. implicit feedback (e.g., browsing history)

• Two popular approaches:

• Collaborative Filtering (CF)

• Content-based

Recommender Systems

3


Context is Essential

• Main idea: users can experience the same item differently depending on the current contextual situation (e.g., weather, season, mood)

• RSs must take into account this information to deliver more useful (perceived) recommendations

4


Context-Aware Recommender Systems

• Context-Aware Recommender Systems (CARSs) improve traditional RSs by adapting their suggestions to the contextual situations of the user and the recommended items

• Example: Google Now

5


Cold-Start Problem

• CARSs suffer from the cold-start problem

• New user problem: How do you recommend to a new user?

• New item problem: How do you recommend a new item with no ratings?

• New context problem: How do you recommend in a new context?

6

1 ? 1 ?

2 5 ?

? 3 ?

3 ? 5 ?

2 5 ?

? 3 ?

5 ? 5 ?

4 5 4 ?

? 3 5 ?

1 ? 12 5? 3

3 ? 52 5? 3

5 ? 54 5 4? 3 5? ? ?

? ? ?1 ? 1

2 5

? 3

3 ? 5

2 5

? 3

5 ? 5

4 5 4

? 3 5


Cold-Start Problem

• CARSs suffer from the cold-start problem

• New user problem: How do you recommend to a new user?

• New item problem: How do you recommend a new item with no ratings?

• New context problem: How do you recommend in a new context?

6

1 ? 1 ?

2 5 ?

? 3 ?

3 ? 5 ?

2 5 ?

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5 ? 5 ?

4 5 4 ?

? 3 5 ?

1 ? 12 5? 3

3 ? 52 5? 3

5 ? 54 5 4? 3 5? ? ?

? ? ?1 ? 1

2 5

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2 5

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5 ? 5

4 5 4

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Focus of this research


Outline

7








Approaches for Cold-Starting CARSs

8

Cold-starting CARSs

8



8

Cold-starting CARSs

… using additional knowledge sources

… better using existing knowledge

8



8

Cold-starting CARSs



Active learning (Elahi et al., 2013)

8



8

Cold-starting CARSs




Cross-domain rec. (Enrich et al., 2013)

8



8

Cold-starting CARSs





Implicit feedback (Koren, 2008)

8



8

Cold-starting CARSs





User / item attributes (Musto et al., 2013)


8



8

Cold-starting CARSs






Selective context acquisition (Baltrunas et al., 2012)


8



8

Cold-starting CARSs






Selective context acquisition (Baltrunas et al., 2012)

Context hierarchy / similarity (Codina et al., 2013)


8


Outline

9








Interaction with the System

10

Welcome screen



10

Registration screen



10

Personality questionnaire



10

Questionnaire results



10

Slide-out navigation menu



10

Suggestions screen



10

Active learning



10

Details screen



10

Routing screen



10

Profile page


Research Hypotheses Tested through User Studies

11



• Personality is useful to elicit more ratings from new users than some state-of-the-art AL strategies based on heuristics

11




• Personality can be exploited for eliciting ratings from new users that lead to an improved system prediction accuracy

11




• Personality can be exploited for eliciting ratings from new users that lead to an improved system prediction accuracy

• Personality can be helpful to acquire ratings from new users which result in recommendations better tailored to the user’s context

11


Research Hypotheses Tested with Offline Experiments

12



• Hybrid CARS algorithms are beneficial for delivering accurate context-aware rating predictions in cold-start situations

12




• Hybrid CARS algorithms can achieve a high recommendation ranking quality in cold-start situations

12




• Hybrid CARS algorithms can achieve a high recommendation ranking quality in cold-start situations

• Parsimonious and adaptive context acquisition can save time and effort of the user by effectively identifying what contextual factors to acquire upon rating an item

12


Outline

13








Hybrid Context-Aware Recommenders

14

• Conjecture: it is possible to adaptively combine multiple CARS algorithms in order to take advantage of their strengths and alleviate their drawbacks in different cold-start situations

• Example:

(user, item, context) tuple

CARS 1

CARS 2

Hybridization Final score

Score

Score

Hybrid CARS


• Matrix Factorization (MF) predicts unknown ratings by discovering some latent features that determine how a user rates an item; items similar to the target user in this latent space are recommended

Matrix Factorization Methods

15

r11 r12 r13 r14

r21 r22 r23 r24

r31 r32 r33 r34

r41 r42 r43 r44

r51 r52 r53 r54

a b c

xyz=

r q p5 x 4 matrix 5 x 3 matrix 3 x 4 matrix

r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z

ȓui = qiTpu




15

r11 r12 r13 r14

r21 r22 r23 r24

r31 r32 r33 r34

r41 r42 r43 r44

r51 r52 r53 r54

a b c

xyz=


r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z

ȓui = qiTpuRating prediction




15

r11 r12 r13 r14

r21 r22 r23 r24

r31 r32 r33 r34

r41 r42 r43 r44

r51 r52 r53 r54

a b c

xyz=


r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z

ȓui = qiTpu

Item preference factor vector




15

r11 r12 r13 r14

r21 r22 r23 r24

r31 r32 r33 r34

r41 r42 r43 r44

r51 r52 r53 r54

a b c

xyz=


r42 = (a, b, c) · (x, y, z) = a * x + b * y + c * z

ȓui = qiTpu User preference factor vector


• CAMF-CC (Context-Aware Matrix Factorization for item categories) is a variant of CAMF that extends standard MF by incorporating baseline parameters for contextual condition-item category pairs

Basic CARS Algorithms CAMF-CC (Baltrunas et al., 2011)

16

ruic1...ck = qiT pu + ri + bu + btcj

j=1

k

∑t∈T (i )∑

qi latent factor vector of item ipu latent factor vector of user u average rating for item ibu baseline for user uT(i) set of categories associated to item ibtcj baseline for item category-contextual condition tcj

ri


Basic CARS Algorithms SPF (Codina et al., 2013)

17

• SPF (Semantic Pre-Filtering) is a contextual pre-filtering method that, given a target contextual situation, uses a standard MF model learnt from all the ratings tagged with contextual situations identical or similar to the target one

• Conjecture: learning the prediction model on a larger number of ratings, even if not obtained exactly in the target context, will help

• Key step: similarity calculation

1 -0.5 2 1

-2 0.5 -2 -1.5

-2 0.5 -1 -1

Condition-to-item co-occurrence matrix

1 -0.96 -0.84

-0.96 1 0.96

-0.84 0.96 1

Cosine similarity between conditions


Basic CARS Algorithms Content-based CAMF-CC

18

• It is a novel variant of CAMF-CC that incorporates additional sources of information about the items, e.g., category or genre information

• Conjecture: alleviates the new item problem of CAMF-CC

ruic1...ck = (qi + xa )a∈A(i )∑ T

pu + ri + bu + btcjj=1

k

∑t∈T (i )∑

qi latent factor vector of item iA(i) set of item attributes xa latent factor vector of item attribute apu latent factor vector of user u average rating for item ibu baseline for user uT(i) set of categories associated to item ibtcj baseline for item category-contextual condition tcj

ri


Basic CARS Algorithms Demographics-based CAMF-CC

19

• It is a novel variant of CAMF-CC that profiles users through known user attributes (e.g., age group, gender, personality traits)

• Conjecture: alleviates the new user problem of CAMF-CC

ruic1...ck = qiT (pu + ya )

a∈A(u )∑ + ri + bu + btcj

j=1

k

∑t∈T (i )∑

qi latent factor vector of item ipu latent factor vector of user uA(u) set of user attributesya latent factor vector of user attribute a overall average ratingbu baseline for user uT(i) set of categories associated to item ibtcj baseline for item category-contextual condition tcj

ri


Hybrid CARS Algorithms Heuristic Switching

• Heuristic Switching uses a stable heuristic to switch between a set of basic CARS algorithms depending on the encountered cold-start situation

• Conjecture: better tackles specific cold-start situations found in CARSs

20

R1: Use content-based CAMF-CC for a new item.

R2: Use demographics-based CAMF-CC for a new user.

R3: Average the predictions of content-based CAMF-CC and demographics-based CAMF-CC for new contextual situations or mixtures of cold-start cases.


• Adaptive Weighted adaptively sums the predictions of the basic algorithms weighted by their estimated accuracies for the user, item and contextual situation in question

• Extends the two-dimensional adaptive RS presented in (Bjørkøy, 2011)

• Conjecture: optimizes adaptation of differently performing CARS algorithms

Hybrid CARS Algorithms Adaptive Weighted (1/2)

21

r

…

∑

…

r1 r 2 r m

a1 a2 am


Hybrid CARS Algorithms Adaptive Weighted (2/2)

22

• Builds for each basic CARS algorithm a new user-item-context error tensor whose entries are the known deviations (errors) of the CARS predictions from the true ratings

• Uses a separate CARS error prediction model for each of these error tensors to predict the errors (accuracies) on a particular (user, item, context) tuple

euic1...ck = (qi + xcici∈IC∑ )T (pu + ycu

cu∈UC∑ )+ ei + bu

qi latent factor vector of item ipu latent factor vector of user uIC subset of item-related contextual conditionsxci latent factor vector of contextual condition ciUC subset of user-related contextual conditionsycu latent factor vector of contextual condition cu average error for item ibu baseline for user uei


Hybrid CARS Algorithms Feature Weighted (1/2)

23

• Feature Weighted adaptively sums the weighted predictions of the basic algorithms with weights estimated using meta-features, i.e., the number of user, item and context ratings

• Is inspired by the Feature-Weighted Linear Stacking (FWLS) algorithm (Sill et al., 2009)

• Conjecture: exploits cold-start conditions under which performance differences between the CARS algorithms can be observed

v11

a1

…

r

∑

r1 r m

∑ ∑

…

…

…

…

f1 fn f1 fn

v11 vn

1 v1m vn

m


Hybrid CARS Algorithms Feature Weighted (2/2)

24

• It extends standard linear stacking, which is a method for linearly combining the predictions of different models m ∈ M:

• Feature Weighted models the weight ŵm as a linear function of some meta-features f ∈ F:

• The rating prediction function is rewritten as:

ruic1...ck = wm

m∈M∑ ruic1...ck

m

wm = v fm

f∈F∑ f (u,i,c1,...,ck )

ruic1...ck = ( v fm

f∈F∑ f (u,i,c1,...,ck ))

m∈M∑ ruic1...ck

m

ruic1...ckm


Evaluation Used Datasets

25

• 4 contextually-tagged rating datasets

STS (Braunhofer et al.,

2013)

CoMoDa (Odić et al.,

2013)

Music (Baltrunas et al.,

2011)

TripAdvisor (www.tripadvisor.

com)

Domain POIs Movies Music POIsRating scale 1-5 1-5 1-5 1-5Ratings 2,534 2,296 4,012 7,154Users 325 121 43 5,487Items 249 1,232 139 1,263Contextual factors 14 12 8 3Contextual conditions 57 49 26 31Contextual situations 931 1,969 26 512User attributes 7 4 10 2Item features 1 7 2 2


Evaluation Evaluation Procedure

26

• Randomly divide the entities (i.e., users, items or contexts) into 10 cross-validation folds

• For each fold k = 1, 2, …, 10

• Use all the ratings except those coming from entities in fold k as training set to build the prediction models

• Calculate the Mean Absolute Error (MAE) and normalized Discounted Cumulative Gain (nDCG) on the test ratings for the entities in fold k

• Advantage: allows to test the models on really cold entities

• Disadvantage: can’t test for different degrees of coldness


Results Recommendation for New Users

27

Basic CARS Algorithms

MAE

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

STS CoMoDa Music TripAdvisor

CAMF-CC SPF Content-CAMF-CC Demographics-CAMF-CC

* *

*

* *

* *

Hybrid CARS Algorithms

MAE

diff

to b

est b

asic

alg

orith

m-0.08-0.06-0.04-0.020.000.020.040.060.080.100.12


Average Weighted Heuristic SwitchingAdaptive Weighted Feature Weighted

*

Stars denote significant differences w.r.t. CAMF-CC (p < 0.05)

Stars denote significant differences w.r.t. best basic CARS algorithm(p < 0.05)


Results Recommendation for New Items

28




MAE

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3



* *

* **

*

*

*

* *


MAE

diff

to b

est b

asic

alg

orith

m-0.08-0.06-0.04-0.020.000.020.040.060.080.100.12



* *

*

**


Results Recommendation under New Contexts

29


MAE

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3



**

* *

** *




MAE

diff

to b

est b

asic

alg

orith

m-0.08-0.06-0.04-0.020.000.020.040.060.080.100.12



* *

**

*

* **


Summary

30

Algorithm Pros ConsAverage Weighted

• Simple and fast to train • Sensitive to poorly performing basic algorithms

• Works only when all basic algorithms are performing equally well


Summary

30




Heuristic Switching

• Simple and fast to train • Can avoid the impact of poorly performing

basic algorithms

• Depends on the manual choice of the heuristic


Summary

30




Heuristic Switching


basic algorithms


Adaptive Weighted

• Adaptively combines the basic algorithms based on their strengths and weaknesses

• Complex and slow to train • Sensitive to the training set used • Optimized for error minimization • Sensitive to poorly performing basic

algorithms


Summary

30




Heuristic Switching


basic algorithms


Adaptive Weighted


• Complex and slow to train • Sensitive to the training set used • Optimized for error minimization • Sensitive to poorly performing basic

algorithmsFeature Weighted


• Robust in all cold-start cases

• Complex and slow to train • Sensitive to the training set used • Optimized for error minimization


Outline

31








• Conjecture: Active Learning (AL), which identifies the most useful items for the target user to rate, can be improved for CARSs by leveraging the user’s personality and by identifying the most useful contextual factors to be entered upon rating these items

Active Learning for CARSs

32

item ratings

item ratings request

approximatedfunction

supervised learning

Active Learning

Passive Learning

user




32

item ratings



supervised learning

Active Learning

Passive Learning

personality (Big-5)

user




32

item ratings



supervised learning

Active Learning

Passive Learning

personality (Big-5)

user

+ context data

+ context data request


Using Personality in Active Learning

• Main idea: people with similar personality are likely to have similar interests (Rentfrow & Gosling, 2003), and thus the incorporation of human personality can help in predicting the items that can be rated by a user

33

Neuroticism Conscientious-ness

Openness

ExtraversionAgreeableness Big Five Personality

Traits


Personality-Based Binary Prediction

• Input: Target user u. Maximum number of items to be returned N. Binary user-item rating matrix B. Candidate set of items to be rated Cu

• Output: List of M <= N top-scoring items for which user u is requested to provide ratings

34

qi latent factor vector of item ipu latent factor vector of user uA(u) set of user u’s attributes (i.e., Big-5 scores)ya latent factor vector of user attribute a average binary rating for item ibu baseline for user uxi

xui = qiT (pu + ya )

a∈A(u )∑ + xi + bu


Parsimonious & Adaptive Context Acquisition

• Main idea: for each user-item pair (u, i), identify the contextual factors that when acquired with u’s rating for i improve most the long term performance of the recommender

• Heuristic: acquire the contextual factors that have the largest impact on rating prediction

• Challenge: how to quantify these impacts?

35


CARS Prediction Model

• We use the new variant of CAMF that we already successfully employed to estimate the rating prediction accuracy of a CARS algorithm

• Advantage: allows to capture latent correlations and patterns between a potentially wide range of knowledge sources ⟹ ideal to derive the usefulness of contextual factors

36

ruic1...ck = (qi + xaa∈A(i )∪C (i )∑ )T ⋅(pu + yb

b∈A(u )∪C (u )∑ )+ ri + bu

qi latent factor vector of item iA(i) set of conventional item attributes (e.g., genre)C(i) set of contextual item attributes (e.g., weather)xa latent factor vector of item attribute apu latent factor vector of user uA(u) set of conventional user attributes (e.g., age)C(u) set of contextual user attributes (e.g., mood)yb latent factor vector of user attribute bṝi average rating for item ibu baseline for user u


Largest Deviation

• Given (u, i), it first measures the “impact” of each contextual condition cj ∈ Cj

by calculating the absolute deviation between the rating prediction when the condition holds (i.e., ȓuicj) and the predicted context-free rating (i.e., ȓui):

where fcj is the normalized frequency of cj

• Finally, it computes for each factor the average of these deviation scores, and selects the contextual factors with the largest average scores

37

wuicj= fcj ruic j − rui ,


Experiments 1 and 2

• 2 user studies involving 108 subjects in the 1st and 51 subjects in the 2nd

• Compared personality-based binary prediction with log(popularity) * entropy and random

• Personality-based binary prediction performed best in terms of:

• Number of acquired ratings

• Rating prediction accuracy

• Quality of context-aware recommendations

38


Experiment 3 Datasets

39

• 3 contextually-tagged rating datasets

CoMoDa (Odić et al.,

2013)

TripAdvisor (www.tripadvisor.

com)

STS (Braunhofer et al.,

2013)

Domain Movies POIs POIsRating scale 1-5 1-5 1-5Ratings 2,098 4,147 2,534Users 112 3,916 325Items 1,189 569 249Contextual factors 12 3 14Contextual conditions 49 31 57Avg. # of conditions / rating 12 3 1.49User attributes 4 2 7Item features 7 2 1


Experiment 3 Evaluation Procedure

40

• Repeated random sub-sampling validation (20 times):



40

25% 50% 25%

Training set Candidate set Testing set


• Randomly partition the ratings into three subsets


• For each user-item pair (u,i) in the candidate set, compute the N most relevant contextual factors and transfer the corresponding rating and context information ruic in the candidate set to the training set as ruic' with c' ⊆ c containing the associated contextual conditions for these factors, if any


40

25% 50% 25%





• Measure user-averaged MAE (U-MAE), Precision@10 and Recall@10 on the testing set, after training the prediction model on the new extended training set



40

25% 50% 25%





• Measure user-averaged MAE (U-MAE), Precision@10 and Recall@10 on the testing set, after training the prediction model on the new extended training set



40

25% 50% 25%




• Repeat


Experiment 3 Evaluation Procedure: Example

41

user-item pairtop two contextual factors

rating transferred to training set

++

=

rating in candidate set



41

(Alice, Skiing)top two contextual factors


++

=




41

(Alice, Skiing)Season and Weather


++

=




41



rAlice Skiing Winter Sunny Warm Morning = 5++

=



41


rAlice Skiing Winter Sunny Warm Morning = 5

rAlice Skiing Winter Sunny = 5

++

=


Experiment 3 Baseline Methods for Evaluation

42

• Mutual Information: given a user-item pair (u,i), computes the relevance for a contextual factor Cj as the mutual information between ratings for items belonging to i’s category (Baltrunas et al., 2012)

• Freeman-Halton Test: calculates the relevance of Cj using the Freeman-Halton test (Odić et al., 2013)

• Minimum Redundancy Maximum Relevance (mRMR): ranks each Cj according to its relevance to the rating variable and redundancy to other contextual factors (Peng et al., 2005)


Experiment 3 Results: Prediction Accuracy

43

CoMoDa

U-M

AE

0.710.720.730.740.750.760.770.780.790.800.810.82

1 2 3 4

Mutual Information Freeman-Halton mRMR Largest Deviation All factors

STS

0.900.910.920.930.940.950.960.970.980.991.00

1 2 3 4

Stars denote significant improvements of Largest Deviation over the other considered algorithms (p < 0.05)

*

* * * *

* * *


Experiment 3 Results: Ranking Quality

44

CoMoDa

Prec

isio

n@10

0.0000

0.0002

0.0004

0.0006

0.0008

0.0010

0.0012

0.0014

0.0016

1 2 3 4


STS

0.0050.0060.0070.0080.0090.0100.0110.0120.0130.0140.0150.016

1 2 3 4

**

**

*

** *



Experiment 3 Results: # of Acquired Conditions

45

STS

Avg

# of

acq

uire

d co

nditi

ons

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1 2 3 4


* * * * * * * ** * *

*



Outline

46








Conclusions

• Novel hybrid recommendation algorithms that, in many cases, effectively alleviate the cold-start problem of CARS

• New personality-based Active Learning rating acquisition algorithm that can better estimate what items a (new) user is able to rate

• Novel parsimonious and adaptive context acquisition algorithm that can identify what contextual factors to acquire from the user upon rating an item, thus minimizing the user’s rating effort

• Comprehensive evaluation of the proposed solutions in cold-start scenarios

47


Future Work

• Additional experiments and datasets

• Improvement of proposed algorithms

• Proactive Active Learning

• Sequential Active Learning

• Gamification approaches

48


Publications Journal Papers

Fernández-Tobías, I., Braunhofer, M., Elahi, M., Cantador, I., & Ricci, F. (2016). Alleviating the New User Problem in Collaborative Filtering by Exploiting Personality Information. User Modeling and User-Adapted Interaction, 1-35. http://dx.doi.org/10.1007/s11257-016-9172-z

Braunhofer, M., Elahi, M., & Ricci, F. (2014). Techniques for cold-starting context-aware mobile recommender systems for tourism. Intelligenza Artificiale, 8(2), 129-143. http://dx.doi.org/10.3233/IA-140069

Braunhofer, M., Kaminskas, M., & Ricci, F. (2013). Location-aware music recommendation. International Journal of Multimedia Information Retrieval, 2(1), 31-44. http://dx.doi.org/10.1007/s13735-012-0032-2

49


Publications Conference Papers

Nasery, M., Braunhofer, M., & Ricci, F. (2016). Recommendations with Optimal Combination of Feature-Based and Item-Based Preferences. To appear in User Modeling, Adaptation, and Personalization. Halifax, Canada: Springer International Publishing

Braunhofer, M., & Ricci, F. (2016). Contextual Information Elicitation in Travel Recommender Systems. In Information and Communication Technologies in Tourism 2016 (pp. 579-592). Bilbao, Spain: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-28231-2_42 (Second Best Research Paper Award)Braunhofer, M., Elahi, M., & Ricci, F. (2015). User Personality and the New User Problem in a Context-Aware Points of Interest Recommender System. In Information and Communication Technologies in Tourism 2015 (pp. 537-549). Lugano, Switzerland: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-14343-9_39

Braunhofer, M., Elahi, M., & Ricci, F. (2014). Usability Assessment of a Context-Aware and Personality-Based Mobile Recommender System. In E-Commerce and Web Technologies (pp. 77-88). Munich, Germany: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-10491-1_9

Braunhofer, M., Elahi, M., Ge, M., & Ricci, F. (2014). Context Dependent Preference Acquisition with Personality-Based Active Learning in Mobile Recommender Systems. In Learning and Collaboration Technologies. Technology-Rich Environments for Learning and Collaboration, Held as Part of HCI International 2014 (pp. 105-116). Heraklion, Crete, Greece: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-07485-6_11

50


Publications Conference Papers (contd.)

Braunhofer, M., Codina, V., & Ricci, F. (2014). Switching hybrid for cold-starting context-aware recommender systems. In Proceedings of the 8th ACM Conference on Recommender systems (pp. 349-352). Foster City, Silicon Valley, California, USA: ACM. http://dx.doi.org/10.1145/2645710.2645757

Braunhofer, M., Elahi, M., Ricci, F., & Schievenin, T. (2013). Context-aware points of interest suggestion with dynamic weather data management. In Information and Communication Technologies in Tourism 2014 (pp. 87-100). Dublin, Ireland: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-03973-2_7

Elahi, M., Braunhofer, M., Ricci, F., & Tkalcic, M. (2013). Personality-based active learning for collaborative filtering recommender systems. In AI*IA 2013: Advances in Artificial Intelligence (pp. 360-371). Turin, Italy: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-03524-6_31

Enrich, M., Braunhofer, M., & Ricci, F. (2013). Cold-Start Management with Cross-Domain Collaborative Filtering and Tags. In E-Commerce and Web Technologies (pp. 101-112). Prague, Czech Republic: Springer Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-642-39878-0_10

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Publications Workshop, Demo & Doctoral Consortium Papers

Braunhofer, M., Fernández-Tobías, I., & Ricci, F. (2015). Parsimonious and Adaptive Contextual Information Acquisition in Recommender Systems. In Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2015, co-located with ACM Conference on Recommender Systems (RecSys 2015). Vienna, Austria: ACM.

Braunhofer, M., Ricci, F., Lamche, B., & Wörndl, W. (2015). A Context-Aware Model for Proactive Recommender Systems in the Tourism Domain. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services Adjunct (pp. 1070-1075). Copenhagen, Denmark: ACM. http://dx.doi.org/10.1145/2786567.2794332

Braunhofer, M. (2014). Hybridisation techniques for cold-starting context-aware recommender systems. In Proceedings of the 8th ACM Conference on Recommender systems, Doctoral Symposium (pp. 405-408). Foster City, Silicon Valley, California, USA: ACM. http://dx.doi.org/10.1145/2645710.2653360

Braunhofer, M. (2014). Hybrid solution of the cold-start problem in context-aware recommender systems. In User Modeling, Adaptation, and Personalization, Doctoral Consortium (pp. 484-489). Aalborg, Denmark: Springer International Publishing. http://dx.doi.org/10.1007/978-3-319-08786-3_44

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Publications Workshop, Demo & Doctoral Consortium Papers (contd.)

Braunhofer, M., Elahi, M., & Ricci, F. (2014). STS: A Context-Aware Mobile Recommender System for Places of Interest. In Extended Proceedings of User Modeling, Adaptation, and Personalization (pp. 75-80). Aalborg, Denmark.

Braunhofer, M., Elahi, M., Ge, M., Ricci, F., & Schievenin, T. (2013). STS: Design of Weather-Aware Mobile Recommender Systems in Tourism. In Proceedings of the First International Workshop on Intelligent User Interfaces: Artificial Intelligence meets Human Computer Interaction (AI*HCI 2013). A workshop of the XIII International Conference of the Italian Association for Artificial Intelligence (AI*IA 2013). Turin, Italy.

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