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PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Techniques for Context-Aware and
Cold-Start Recommendations
Matthias Braunh...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Outline
2
• Context-Aware Recommenders and the Cold-Start Problem
• State o...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Outline
2
• Context-Aware Recommenders and the Cold-Start Problem
• State o...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Recommender Systems (RSs) are information filtering and decision
support t...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Context is Essential
• Main idea: users can experience the same item differe...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Context-Aware Recommender Systems
• Context-Aware Recommender Systems (CARS...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Cold-Start Problem
• CARSs suffer from the cold-start problem

• New user pr...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Cold-Start Problem
• CARSs suffer from the cold-start problem

• New user pr...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Outline
7
• Context-Aware Recommenders and the Cold-Start Problem
• State o...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
8
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
… using additional...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
… using additional...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
… using additional...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
… using additional...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
… using additional...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
… using additional...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Approaches for Cold-Starting CARSs
8
Cold-starting CARSs
… using additional...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Outline
9
• Context-Aware Recommenders and the Cold-Start Problem
• State o...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Welcome screen
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Registration screen
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Personality questionnaire
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Questionnaire results
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Slide-out navigation menu
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Suggestions screen
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Active learning
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Details screen
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Routing screen
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Interaction with the System
10
Profile page
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested through User Studies
11
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested through User Studies
• Personality is useful to ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested through User Studies
• Personality is useful to ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested through User Studies
• Personality is useful to ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested with Offline Experiments
12
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested with Offline Experiments
• Hybrid CARS algorithms...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested with Offline Experiments
• Hybrid CARS algorithms...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Research Hypotheses
Tested with Offline Experiments
• Hybrid CARS algorithms...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Outline
13
• Context-Aware Recommenders and the Cold-Start Problem
• State ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid Context-Aware Recommenders
14
• Conjecture: it is possible to adapti...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Matrix Factorization (MF) predicts unknown ratings by discovering some
la...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Matrix Factorization (MF) predicts unknown ratings by discovering some
la...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Matrix Factorization (MF) predicts unknown ratings by discovering some
la...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Matrix Factorization (MF) predicts unknown ratings by discovering some
la...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• CAMF-CC (Context-Aware Matrix Factorization for item categories) is a
var...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• CAMF-CC (Context-Aware Matrix Factorization for item categories) is a
var...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• CAMF-CC (Context-Aware Matrix Factorization for item categories) is a
var...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• CAMF-CC (Context-Aware Matrix Factorization for item categories) is a
var...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• CAMF-CC (Context-Aware Matrix Factorization for item categories) is a
var...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Basic CARS Algorithms
SPF (Codina et al., 2013)
17
• SPF (Semantic Pre-Filt...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Basic CARS Algorithms
Content-based CAMF-CC
18
• It is a novel variant of C...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Basic CARS Algorithms
Content-based CAMF-CC
18
• It is a novel variant of C...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Basic CARS Algorithms
Demographics-based CAMF-CC
19
• It is a novel variant...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Basic CARS Algorithms
Demographics-based CAMF-CC
19
• It is a novel variant...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Heuristic Switching
• Heuristic Switching uses a sta...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Adaptive Weighted adaptively sums the predictions of the basic algorithms...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Adaptive Weighted (2/2)
22
• Builds for each basic C...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Adaptive Weighted (2/2)
22
• Builds for each basic C...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Adaptive Weighted (2/2)
22
• Builds for each basic C...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Adaptive Weighted (2/2)
22
• Builds for each basic C...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Adaptive Weighted (2/2)
22
• Builds for each basic C...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Adaptive Weighted (2/2)
22
• Builds for each basic C...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Adaptive Weighted (2/2)
22
• Builds for each basic C...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Feature Weighted (1/2)
23
• Feature Weighted adaptiv...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Feature Weighted (2/2)
24
• It extends standard line...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Feature Weighted (2/2)
24
• It extends standard line...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Feature Weighted (2/2)
24
• It extends standard line...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Feature Weighted (2/2)
24
• It extends standard line...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Feature Weighted (2/2)
24
• It extends standard line...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Hybrid CARS Algorithms
Feature Weighted (2/2)
24
• It extends standard line...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Evaluation
Used Datasets
25
• 4 contextually-tagged rating datasets
STS
(Br...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Evaluation
Evaluation Procedure
26
• Randomly divide the entities (i.e., us...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Results
Recommendation for New Users
27
Basic CARS Algorithms
MAE
0.5
0.6
0...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Results
Recommendation for New Items
28
Stars denote significant differences...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Results
Recommendation under New Contexts
29
Basic CARS Algorithms
MAE
0.5
...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Summary
30
Algorithm Pros Cons
Average
Weighted
• Simple and fast to train ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Summary
30
Algorithm Pros Cons
Average
Weighted
• Simple and fast to train ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Summary
30
Algorithm Pros Cons
Average
Weighted
• Simple and fast to train ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Summary
30
Algorithm Pros Cons
Average
Weighted
• Simple and fast to train ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Outline
31
• Context-Aware Recommenders and the Cold-Start Problem
• State ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Conjecture: Active Learning (AL), which identifies the most useful items f...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Conjecture: Active Learning (AL), which identifies the most useful items f...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Conjecture: Active Learning (AL), which identifies the most useful items f...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Using Personality in Active Learning
• Main idea: people with similar perso...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Personality-Based Binary Prediction
• Input: Target user u. Maximum number ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Personality-Based Binary Prediction
• Input: Target user u. Maximum number ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Personality-Based Binary Prediction
• Input: Target user u. Maximum number ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Parsimonious & Adaptive Context Acquisition
• Main idea: for each user-item...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
CARS Prediction Model
• We use the new variant of CAMF that we already succ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Largest Deviation
• Given (u, i), it first measures the “impact” of each con...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Largest Deviation
• Given (u, i), it first measures the “impact” of each con...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Largest Deviation
• Given (u, i), it first measures the “impact” of each con...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Largest Deviation
• Given (u, i), it first measures the “impact” of each con...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiments 1 and 2
• 2 user studies involving 108 subjects in the 1st and ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Datasets
39
• 3 contextually-tagged rating datasets
CoMoDa
(Od...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Evaluation Procedure
40
• Repeated random sub-sampling validat...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Evaluation Procedure
40
25% 50% 25%
Training set Candidate set...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• For each user-item pair (u,i) in the candidate set, compute the N most re...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• For each user-item pair (u,i) in the candidate set, compute the N most re...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• For each user-item pair (u,i) in the candidate set, compute the N most re...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• For each user-item pair (u,i) in the candidate set, compute the N most re...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• For each user-item pair (u,i) in the candidate set, compute the N most re...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• For each user-item pair (u,i) in the candidate set, compute the N most re...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Measure user-averaged MAE (U-MAE), Precision@10 and Recall@10 on the test...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
• Measure user-averaged MAE (U-MAE), Precision@10 and Recall@10 on the test...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Evaluation Procedure: Example
41
user-item pair
top two contex...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Evaluation Procedure: Example
41
(Alice, Skiing)
top two conte...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Evaluation Procedure: Example
41
(Alice, Skiing)
Season and We...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Evaluation Procedure: Example
41
(Alice, Skiing)
Season and We...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Evaluation Procedure: Example
41
(Alice, Skiing)
Season and We...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Baseline Methods for Evaluation
42
• Mutual Information: given...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Results: Prediction Accuracy
43
CoMoDa
U-MAE
0.71
0.72
0.73
0....
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Results: Ranking Quality
44
CoMoDa
Precision@10
0.0000
0.0002
...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Experiment 3
Results: # of Acquired Conditions
45
STS
Avg#ofacquiredconditi...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Outline
46
• Context-Aware Recommenders and the Cold-Start Problem
• State ...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Conclusions
• Novel hybrid recommendation algorithms that, in many cases, e...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Future Work
• Additional experiments and datasets

• Improvement of propose...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Publications
Journal Papers
Fernández-Tobías, I., Braunhofer, M., Elahi, M....
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Publications
Conference Papers
Nasery, M., Braunhofer, M., & Ricci, F. (201...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Publications
Conference Papers (contd.)
Braunhofer, M., Codina, V., & Ricci...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Publications
Workshop, Demo & Doctoral Consortium Papers
Braunhofer, M., Fe...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
Publications
Workshop, Demo & Doctoral Consortium Papers (contd.)
Braunhofe...
PhD Thesis Defense, 28th Ph.D. Cycle, May 2016
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Techniques for Context-Aware and Cold-Start Recommendations

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Context-aware recommender systems better identify interesting items for users by adapting their suggestions to the specific contextual situations, e.g., to the current weather, if an excursion is to be recommended . But, the cold-start problem may jeopardise the quality of the recommendations: for users, items or contextual situations that are new to the system, recommendations are hard to compute. We have developed a number of novel techniques to tame this problem, and in particular, new hybrid algorithms that combine several, simpler, algorithms in order to exploit their strengths and avoid their weaknesses. We have also developed algorithms for actively identifying the most useful preference information to ask the user in order to bootstrap the system. Our results obtained from a series of offline and online experiments reveal that the proposed techniques can effectively alleviate the cold-start problem of context-aware recommender systems.

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Techniques for Context-Aware and Cold-Start Recommendations

  1. 1. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Techniques for Context-Aware and Cold-Start Recommendations Matthias Braunhofer Supervisor: Prof. Francesco Ricci
 Free University of Bozen - Bolzano Piazza Domenicani 3, 39100 Bolzano, Italy mbraunhofer@unibz.it
  2. 2. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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
  3. 3. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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
  4. 4. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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
  5. 5. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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
  6. 6. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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
  7. 7. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ? 1 2 5 ? 3 3 ? 5 2 5 ? 3 5 ? 5 4 5 4 ? 3 5 ? ? ? ? ? ? 1 ? 1 2 5 ? 3 3 ? 5 2 5 ? 3 5 ? 5 4 5 4 ? 3 5
  8. 8. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ? 1 2 5 ? 3 3 ? 5 2 5 ? 3 5 ? 5 4 5 4 ? 3 5 ? ? ? ? ? ? 1 ? 1 2 5 ? 3 3 ? 5 2 5 ? 3 5 ? 5 4 5 4 ? 3 5 Focus of this research
  9. 9. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Outline 7 • 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
  10. 10. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Approaches for Cold-Starting CARSs 8 Cold-starting CARSs 8
  11. 11. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Approaches for Cold-Starting CARSs 8 Cold-starting CARSs … using additional knowledge sources … better using existing knowledge 8
  12. 12. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Approaches for Cold-Starting CARSs 8 Cold-starting CARSs … using additional knowledge sources … better using existing knowledge Active learning (Elahi et al., 2013) 8
  13. 13. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Approaches for Cold-Starting CARSs 8 Cold-starting CARSs … using additional knowledge sources … better using existing knowledge Active learning (Elahi et al., 2013) Cross-domain rec. (Enrich et al., 2013) 8
  14. 14. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Approaches for Cold-Starting CARSs 8 Cold-starting CARSs … using additional knowledge sources … better using existing knowledge Active learning (Elahi et al., 2013) Cross-domain rec. (Enrich et al., 2013) Implicit feedback (Koren, 2008) 8
  15. 15. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Approaches for Cold-Starting CARSs 8 Cold-starting CARSs … using additional knowledge sources … better using existing knowledge Active learning (Elahi et al., 2013) Cross-domain rec. (Enrich et al., 2013) User / item attributes (Musto et al., 2013) Implicit feedback (Koren, 2008) 8
  16. 16. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Approaches for Cold-Starting CARSs 8 Cold-starting CARSs … using additional knowledge sources … better using existing knowledge Active learning (Elahi et al., 2013) Cross-domain rec. (Enrich et al., 2013) User / item attributes (Musto et al., 2013) Selective context acquisition (Baltrunas et al., 2012) Implicit feedback (Koren, 2008) 8
  17. 17. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Approaches for Cold-Starting CARSs 8 Cold-starting CARSs … using additional knowledge sources … better using existing knowledge Active learning (Elahi et al., 2013) Cross-domain rec. (Enrich et al., 2013) User / item attributes (Musto et al., 2013) Selective context acquisition (Baltrunas et al., 2012) Context hierarchy / similarity (Codina et al., 2013) Implicit feedback (Koren, 2008) 8
  18. 18. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Outline 9 • 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
  19. 19. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Interaction with the System 10 Welcome screen
  20. 20. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Interaction with the System 10 Registration screen
  21. 21. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Interaction with the System 10 Personality questionnaire
  22. 22. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Interaction with the System 10 Questionnaire results
  23. 23. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Interaction with the System 10 Slide-out navigation menu
  24. 24. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Interaction with the System 10 Suggestions screen
  25. 25. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Interaction with the System 10 Active learning
  26. 26. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Interaction with the System 10 Details screen
  27. 27. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Interaction with the System 10 Routing screen
  28. 28. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Interaction with the System 10 Profile page
  29. 29. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Research Hypotheses Tested through User Studies 11
  30. 30. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Research Hypotheses Tested through User Studies • Personality is useful to elicit more ratings from new users than some state-of- the-art AL strategies based on heuristics 11
  31. 31. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Research Hypotheses Tested through User Studies • Personality is useful to elicit more ratings from new users than some state-of- the-art AL strategies based on heuristics • Personality can be exploited for eliciting ratings from new users that lead to an improved system prediction accuracy 11
  32. 32. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Research Hypotheses Tested through User Studies • Personality is useful to elicit more ratings from new users than some state-of- the-art AL strategies based on heuristics • 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
  33. 33. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Research Hypotheses Tested with Offline Experiments 12
  34. 34. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Research Hypotheses Tested with Offline Experiments • Hybrid CARS algorithms are beneficial for delivering accurate context-aware rating predictions in cold-start situations 12
  35. 35. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Research Hypotheses Tested with Offline Experiments • Hybrid CARS algorithms are beneficial for delivering accurate context-aware rating predictions in cold-start situations • Hybrid CARS algorithms can achieve a high recommendation ranking quality in cold-start situations 12
  36. 36. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Research Hypotheses Tested with Offline Experiments • Hybrid CARS algorithms are beneficial for delivering accurate context-aware rating predictions in cold-start situations • 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
  37. 37. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Outline 13 • 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
  38. 38. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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
  39. 39. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 x y z= r q p 5 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 = qi Tpu
  40. 40. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 x y z= r q p 5 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 = qi TpuRating prediction
  41. 41. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 x y z= r q p 5 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 = qi Tpu Item preference factor vector
  42. 42. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 x y z= r q p 5 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 = qi Tpu User preference factor vector
  43. 43. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 ˆruic1...ck = qi T pu + ri + bu + btcj j=1 k ∑ t∈T (i) ∑ qi latent factor vector of item i pu latent factor vector of user u average rating for item i bu baseline for user u T(i) set of categories associated to item i btcj baseline for item category-contextual condition tcj ri
  44. 44. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 ˆruic1...ck = qi T pu + ri + bu + btcj j=1 k ∑ t∈T (i) ∑ qi latent factor vector of item i pu latent factor vector of user u average rating for item i bu baseline for user u T(i) set of categories associated to item i btcj baseline for item category-contextual condition tcj ri
  45. 45. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 ˆruic1...ck = qi T pu + ri + bu + btcj j=1 k ∑ t∈T (i) ∑ qi latent factor vector of item i pu latent factor vector of user u average rating for item i bu baseline for user u T(i) set of categories associated to item i btcj baseline for item category-contextual condition tcj ri
  46. 46. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 ˆruic1...ck = qi T pu + ri + bu + btcj j=1 k ∑ t∈T (i) ∑ qi latent factor vector of item i pu latent factor vector of user u average rating for item i bu baseline for user u T(i) set of categories associated to item i btcj baseline for item category-contextual condition tcj ri
  47. 47. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 ˆruic1...ck = qi T pu + ri + bu + btcj j=1 k ∑ t∈T (i) ∑ qi latent factor vector of item i pu latent factor vector of user u average rating for item i bu baseline for user u T(i) set of categories associated to item i btcj baseline for item category-contextual condition tcj ri
  48. 48. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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
  49. 49. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆruic1...ck = (qi + xa ) a∈A(i) ∑ T pu + ri + bu + btcj j=1 k ∑ t∈T (i) ∑ qi latent factor vector of item i A(i) set of item attributes xa latent factor vector of item attribute a pu latent factor vector of user u average rating for item i bu baseline for user u T(i) set of categories associated to item i btcj baseline for item category-contextual condition tcj ri
  50. 50. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆruic1...ck = (qi + xa ) a∈A(i) ∑ T pu + ri + bu + btcj j=1 k ∑ t∈T (i) ∑ qi latent factor vector of item i A(i) set of item attributes xa latent factor vector of item attribute a pu latent factor vector of user u average rating for item i bu baseline for user u T(i) set of categories associated to item i btcj baseline for item category-contextual condition tcj ri
  51. 51. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆruic1...ck = qi T (pu + ya ) a∈A(u) ∑ + ri + bu + btcj j=1 k ∑ t∈T (i) ∑ qi latent factor vector of item i pu latent factor vector of user u A(u) set of user attributes ya latent factor vector of user attribute a overall average rating bu baseline for user u T(i) set of categories associated to item i btcj baseline for item category-contextual condition tcj ri
  52. 52. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆruic1...ck = qi T (pu + ya ) a∈A(u) ∑ + ri + bu + btcj j=1 k ∑ t∈T (i) ∑ qi latent factor vector of item i pu latent factor vector of user u A(u) set of user attributes ya latent factor vector of user attribute a overall average rating bu baseline for user u T(i) set of categories associated to item i btcj baseline for item category-contextual condition tcj ri
  53. 53. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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.
  54. 54. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 ˆr … ∑ … ˆr1 ˆr2 ˆrm ˆa1 ˆa2 ˆam
  55. 55. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆeuic1...ck = (qi + xci ci∈IC ∑ )T (pu + ycu cu ∈UC ∑ )+ ei + bu qi latent factor vector of item i pu latent factor vector of user u IC subset of item-related contextual conditions xci latent factor vector of contextual condition ci UC subset of user-related contextual conditions ycu latent factor vector of contextual condition cu average error for item i bu baseline for user u ei
  56. 56. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆeuic1...ck = (qi + xci ci∈IC ∑ )T (pu + ycu cu ∈UC ∑ )+ ei + bu qi latent factor vector of item i pu latent factor vector of user u IC subset of item-related contextual conditions xci latent factor vector of contextual condition ci UC subset of user-related contextual conditions ycu latent factor vector of contextual condition cu average error for item i bu baseline for user u ei
  57. 57. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆeuic1...ck = (qi + xci ci∈IC ∑ )T (pu + ycu cu ∈UC ∑ )+ ei + bu qi latent factor vector of item i pu latent factor vector of user u IC subset of item-related contextual conditions xci latent factor vector of contextual condition ci UC subset of user-related contextual conditions ycu latent factor vector of contextual condition cu average error for item i bu baseline for user u ei
  58. 58. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆeuic1...ck = (qi + xci ci∈IC ∑ )T (pu + ycu cu ∈UC ∑ )+ ei + bu qi latent factor vector of item i pu latent factor vector of user u IC subset of item-related contextual conditions xci latent factor vector of contextual condition ci UC subset of user-related contextual conditions ycu latent factor vector of contextual condition cu average error for item i bu baseline for user u ei
  59. 59. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆeuic1...ck = (qi + xci ci∈IC ∑ )T (pu + ycu cu ∈UC ∑ )+ ei + bu qi latent factor vector of item i pu latent factor vector of user u IC subset of item-related contextual conditions xci latent factor vector of contextual condition ci UC subset of user-related contextual conditions ycu latent factor vector of contextual condition cu average error for item i bu baseline for user u ei
  60. 60. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆeuic1...ck = (qi + xci ci∈IC ∑ )T (pu + ycu cu ∈UC ∑ )+ ei + bu qi latent factor vector of item i pu latent factor vector of user u IC subset of item-related contextual conditions xci latent factor vector of contextual condition ci UC subset of user-related contextual conditions ycu latent factor vector of contextual condition cu average error for item i bu baseline for user u ei
  61. 61. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆeuic1...ck = (qi + xci ci∈IC ∑ )T (pu + ycu cu ∈UC ∑ )+ ei + bu qi latent factor vector of item i pu latent factor vector of user u IC subset of item-related contextual conditions xci latent factor vector of contextual condition ci UC subset of user-related contextual conditions ycu latent factor vector of contextual condition cu average error for item i bu baseline for user u ei
  62. 62. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆv1 1 ˆa1 … ˆr ∑ ˆr1 ˆrm ∑ ∑ … … … … f1 fn f1 fn ˆv1 1 ˆvn 1 ˆv1 m ˆvn m
  63. 63. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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: ˆruic1...ck = ˆwm m∈M ∑ ˆruic1...ck m ˆwm = ˆvf m f ∈F ∑ f (u,i,c1,...,ck ) ˆruic1...ck = ( ˆvf m f ∈F ∑ f (u,i,c1,...,ck )) m∈M ∑ ˆruic1...ck m ˆruic1...ck m
  64. 64. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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: ˆruic1...ck = ˆwm m∈M ∑ ˆruic1...ck m ˆwm = ˆvf m f ∈F ∑ f (u,i,c1,...,ck ) ˆruic1...ck = ( ˆvf m f ∈F ∑ f (u,i,c1,...,ck )) m∈M ∑ ˆruic1...ck m ˆruic1...ck m
  65. 65. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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: ˆruic1...ck = ˆwm m∈M ∑ ˆruic1...ck m ˆwm = ˆvf m f ∈F ∑ f (u,i,c1,...,ck ) ˆruic1...ck = ( ˆvf m f ∈F ∑ f (u,i,c1,...,ck )) m∈M ∑ ˆruic1...ck m ˆruic1...ck m
  66. 66. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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: ˆruic1...ck = ˆwm m∈M ∑ ˆruic1...ck m ˆwm = ˆvf m f ∈F ∑ f (u,i,c1,...,ck ) ˆruic1...ck = ( ˆvf m f ∈F ∑ f (u,i,c1,...,ck )) m∈M ∑ ˆruic1...ck m ˆruic1...ck m
  67. 67. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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: ˆruic1...ck = ˆwm m∈M ∑ ˆruic1...ck m ˆwm = ˆvf m f ∈F ∑ f (u,i,c1,...,ck ) ˆruic1...ck = ( ˆvf m f ∈F ∑ f (u,i,c1,...,ck )) m∈M ∑ ˆruic1...ck m ˆruic1...ck m
  68. 68. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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: ˆruic1...ck = ˆwm m∈M ∑ ˆruic1...ck m ˆwm = ˆvf m f ∈F ∑ f (u,i,c1,...,ck ) ˆruic1...ck = ( ˆvf m f ∈F ∑ f (u,i,c1,...,ck )) m∈M ∑ ˆruic1...ck m ˆruic1...ck m
  69. 69. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 POIs Rating scale 1-5 1-5 1-5 1-5 Ratings 2,534 2,296 4,012 7,154 Users 325 121 43 5,487 Items 249 1,232 139 1,263 Contextual factors 14 12 8 3 Contextual conditions 57 49 26 31 Contextual situations 931 1,969 26 512 User attributes 7 4 10 2 Item features 1 7 2 2
  70. 70. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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
  71. 71. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 MAEdifftobestbasicalgorithm -0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 0.12 STS CoMoDa Music TripAdvisor Average Weighted Heuristic Switching Adaptive 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)
  72. 72. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Results Recommendation for New Items 28 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) 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 MAEdifftobestbasicalgorithm -0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 0.12 STS CoMoDa Music TripAdvisor Average Weighted Heuristic Switching Adaptive Weighted Feature Weighted * * * * *
  73. 73. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Results Recommendation under New Contexts 29 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 * * * * * * * 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) Hybrid CARS Algorithms MAEdifftobestbasicalgorithm -0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 0.12 STS CoMoDa Music TripAdvisor Average Weighted Heuristic Switching Adaptive Weighted Feature Weighted * * * * * * * *
  74. 74. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Summary 30 Algorithm Pros Cons Average Weighted • Simple and fast to train • Sensitive to poorly performing basic algorithms • Works only when all basic algorithms are performing equally well
  75. 75. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Summary 30 Algorithm Pros Cons Average Weighted • Simple and fast to train • Sensitive to poorly performing basic algorithms • Works only when all basic algorithms are performing equally well Heuristic Switching • Simple and fast to train • Can avoid the impact of poorly performing basic algorithms • Depends on the manual choice of the heuristic
  76. 76. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Summary 30 Algorithm Pros Cons Average Weighted • Simple and fast to train • Sensitive to poorly performing basic algorithms • Works only when all basic algorithms are performing equally well Heuristic Switching • Simple and fast to train • Can avoid the impact of poorly performing basic algorithms • Depends on the manual choice of the heuristic 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
  77. 77. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Summary 30 Algorithm Pros Cons Average Weighted • Simple and fast to train • Sensitive to poorly performing basic algorithms • Works only when all basic algorithms are performing equally well Heuristic Switching • Simple and fast to train • Can avoid the impact of poorly performing basic algorithms • Depends on the manual choice of the heuristic 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 Feature Weighted • Adaptively combines the basic algorithms based on their strengths and weaknesses • Robust in all cold-start cases • Complex and slow to train • Sensitive to the training set used • Optimized for error minimization
  78. 78. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Outline 31 • 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
  79. 79. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 approximated
 function supervised learning Active Learning Passive Learning user
  80. 80. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 approximated
 function supervised learning Active Learning Passive Learning personality (Big-5) user
  81. 81. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 approximated
 function supervised learning Active Learning Passive Learning personality (Big-5) user + context data + context data request
  82. 82. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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
  83. 83. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 i pu latent factor vector of user u A(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 i bu baseline for user u xi ˆxui = qi T (pu + ya ) a∈A(u) ∑ + xi + bu
  84. 84. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 i pu latent factor vector of user u A(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 i bu baseline for user u xi ˆxui = qi T (pu + ya ) a∈A(u) ∑ + xi + bu
  85. 85. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 i pu latent factor vector of user u A(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 i bu baseline for user u xi ˆxui = qi T (pu + ya ) a∈A(u) ∑ + xi + bu
  86. 86. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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
  87. 87. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆruic1...ck = (qi + xa a∈A(i)∪C(i) ∑ )T ⋅(pu + yb b∈A(u)∪C(u) ∑ )+ ri + bu qi latent factor vector of item i A(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 a pu latent factor vector of user u A(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 b ṝi average rating for item i bu baseline for user u
  88. 88. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆwuicj = fcj ˆruicj − ˆrui ,
  89. 89. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆwuicj = fcj ˆruicj − ˆrui ,
  90. 90. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆwuicj = fcj ˆruicj − ˆrui ,
  91. 91. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 ˆwuicj = fcj ˆruicj − ˆrui ,
  92. 92. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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
  93. 93. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 POIs Rating scale 1-5 1-5 1-5 Ratings 2,098 4,147 2,534 Users 112 3,916 325 Items 1,189 569 249 Contextual factors 12 3 14 Contextual conditions 49 31 57 Avg. # of conditions / rating 12 3 1.49 User attributes 4 2 7 Item features 7 2 1
  94. 94. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Experiment 3 Evaluation Procedure 40 • Repeated random sub-sampling validation (20 times):
  95. 95. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Experiment 3 Evaluation Procedure 40 25% 50% 25% Training set Candidate set Testing set • Repeated random sub-sampling validation (20 times): • Randomly partition the ratings into three subsets
  96. 96. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 Experiment 3 Evaluation Procedure 40 25% 50% 25% Training set Candidate set Testing set • Repeated random sub-sampling validation (20 times): • Randomly partition the ratings into three subsets
  97. 97. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 Experiment 3 Evaluation Procedure 40 25% 50% 25% Training set Candidate set Testing set • Repeated random sub-sampling validation (20 times): • Randomly partition the ratings into three subsets
  98. 98. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 Experiment 3 Evaluation Procedure 40 25% 50% 25% Training set Candidate set Testing set • Repeated random sub-sampling validation (20 times): • Randomly partition the ratings into three subsets
  99. 99. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 Experiment 3 Evaluation Procedure 40 25% 50% 25% Training set Candidate set Testing set • Repeated random sub-sampling validation (20 times): • Randomly partition the ratings into three subsets
  100. 100. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 Experiment 3 Evaluation Procedure 40 25% 50% 25% Training set Candidate set Testing set • Repeated random sub-sampling validation (20 times): • Randomly partition the ratings into three subsets
  101. 101. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 Experiment 3 Evaluation Procedure 40 25% 50% 25% Training set Candidate set Testing set • Repeated random sub-sampling validation (20 times): • Randomly partition the ratings into three subsets
  102. 102. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 • 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 Experiment 3 Evaluation Procedure 40 25% 50% 25% Training set Candidate set Testing set • Repeated random sub-sampling validation (20 times): • Randomly partition the ratings into three subsets
  103. 103. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 • 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 • 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 Experiment 3 Evaluation Procedure 40 25% 50% 25% Training set Candidate set Testing set • Repeated random sub-sampling validation (20 times): • Randomly partition the ratings into three subsets • Repeat
  104. 104. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Experiment 3 Evaluation Procedure: Example 41 user-item pair top two contextual factors rating transferred to training set + + = rating in candidate set
  105. 105. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Experiment 3 Evaluation Procedure: Example 41 (Alice, Skiing) top two contextual factors rating transferred to training set + + = rating in candidate set
  106. 106. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Experiment 3 Evaluation Procedure: Example 41 (Alice, Skiing) Season and Weather rating transferred to training set + + = rating in candidate set
  107. 107. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Experiment 3 Evaluation Procedure: Example 41 (Alice, Skiing) Season and Weather rating transferred to training set rAlice Skiing Winter Sunny Warm Morning = 5+ + =
  108. 108. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Experiment 3 Evaluation Procedure: Example 41 (Alice, Skiing) Season and Weather rAlice Skiing Winter Sunny Warm Morning = 5 rAlice Skiing Winter Sunny = 5 + + =
  109. 109. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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)
  110. 110. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Experiment 3 Results: Prediction Accuracy 43 CoMoDa U-MAE 0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.80 0.81 0.82 1 2 3 4 Mutual Information Freeman-Halton mRMR Largest Deviation All factors STS 0.90 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99 1.00 1 2 3 4 Stars denote significant improvements of Largest Deviation over the other considered algorithms 
 (p < 0.05) * * * * * * * *
  111. 111. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Experiment 3 Results: Ranking Quality 44 CoMoDa Precision@10 0.0000 0.0002 0.0004 0.0006 0.0008 0.0010 0.0012 0.0014 0.0016 1 2 3 4 Mutual Information Freeman-Halton mRMR Largest Deviation All factors STS 0.005 0.006 0.007 0.008 0.009 0.010 0.011 0.012 0.013 0.014 0.015 0.016 1 2 3 4 * * * * * * * * Stars denote significant improvements of Largest Deviation over the other considered algorithms 
 (p < 0.05)
  112. 112. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Experiment 3 Results: # of Acquired Conditions 45 STS Avg#ofacquiredconditions 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1 2 3 4 Mutual Information Freeman-Halton mRMR Largest Deviation All factors * * * * * * * * * * * * Stars denote significant improvements of Largest Deviation over the other considered algorithms 
 (p < 0.05)
  113. 113. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Outline 46 • 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
  114. 114. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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
  115. 115. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Future Work • Additional experiments and datasets • Improvement of proposed algorithms • Proactive Active Learning • Sequential Active Learning • Gamification approaches 48
  116. 116. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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
  117. 117. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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
  118. 118. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 51
  119. 119. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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 52
  120. 120. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 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. 53
  121. 121. PhD Thesis Defense, 28th Ph.D. Cycle, May 2016 Questions? Thank you.

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