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[Decisions2013@RecSys]The Role of Emotions in Context-aware Recommendation
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[Decisions2013@RecSys]The Role of Emotions in Context-aware Recommendation

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Context-aware recommender systems try to adapt to users' preferences across different contexts and have been proven to provide better predictive performance in a number of domains. Emotion is one of …

Context-aware recommender systems try to adapt to users' preferences across different contexts and have been proven to provide better predictive performance in a number of domains. Emotion is one of the most popular contextual variables, but few researchers have explored how emotions take effect in recommendations -- especially the usage of the emotional variables other than the effectiveness alone. In this paper, we explore the role of emotions in context-aware recommendation algorithms. More specifically, we evaluate two types of popular context-aware recommendation algorithms -- context-aware splitting approaches and differential context modeling. We examine predictive performance, and also explore the usage of emotions to discover how emotional features interact with those context-aware recommendation algorithms in the recommendation process.

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  • 1. The Role of Emotions in Context-aware Recommendation Yong Zheng, Robin Burke, Bamshad Mobasher Center for Web Intelligence, DePaul University, USA Decisions@RecSys 2013, Hong Kong, Oct 12
  • 2. • Recommender Systems (RS) & Affective RS • Context-aware Recommender Systems (CARS) • The Role of Emotions in CARS • Explore The Roles by CARS Algorithms • Experimental Results • Conclusions and Future Work
  • 3. • Recommender Systems (RS) & Affective RS • Context-aware Recommender Systems (CARS) • The Role of Emotions in CARS • Explore The Roles by CARS Algorithms • Experimental Results • Conclusions and Future Work
  • 4. Recommender Systems (RS) • Information overload problem • Adapt user preference • Provide a list of recommendations • Assist decision makings E-Commerce (Amazon.com)
  • 5. Social RS (Twitter) Tagging RS (Flickr) Examples in other domains: Netflix, Pandora, Yelp, etc
  • 6. Affective Recommender Systems • Affective computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects. It is an interdisciplinary field spanning computer sciences, psychology, and cognitive science. • Decision Making = Rational + Emotional (Daniel Kahnemann, Economic Nobel Prize 2002) • Affective Recommender System takes emotional effects into consideration to further assist decision makings and recommendations. E.g. music preferences by emotional effect M. Tkalcic, A. Kosir, J. Tasic. "Affective recommender systems: the role of emotions in recommender systems." Decisions@RecSys 2011
  • 7. Take Emotions into RS • Content-based Frameworks Semantic emotions can be extracted from tags (affective tags), reviews (review or opinion mining), user tweets, metadata, etc • Collaborative Filtering In a way of “collaborative”: see how other users prefer with similar emotions; or see how other items user may prefer within similar emotions • Context-aware Recommender Systems It is a multidimensional approach, where emotions are deemed as additional dimensions
  • 8. • Recommender Systems (RS) & Affective RS • Context-aware Recommender Systems (CARS) • The Role of Emotions in CARS • Explore The Roles by CARS Algorithms • Experimental Results • Conclusions and Future Work
  • 9. Context-aware RS (CARS) • Traditional RS: Users × Items  Ratings • CARS: Users × Items × Contexts  Ratings
  • 10. Context-aware RS (CARS) • Example: Tour Plan Recommender Yu, Chien-Chih and Chang, Hsiao-ping, "Towards Context-Aware Recommendation for Personalized Mobile Travel Planning". International Conference on Context-Aware Systems and Applications, ICCASA 2012
  • 11. • Recommender Systems (RS) & Affective RS • Context-aware Recommender Systems (CARS) • The Role of Emotions in CARS • Explore The Roles by CARS Algorithms • Experimental Results • Conclusions and Future Work
  • 12. The Role of Emotions in CARS • Several research has considered emotions as contexts and demonstrate the improved efficiency in contextual recommendation • But what is the role of emotions? Improved effectiveness in terms of MAE, RMSE, Precision, etc cannot fully reveal the roles.
  • 13. The Role of Emotions in CARS Definition: the role of emotions • Effectiveness: how effective the emotions are? Comparison among emotion only, no emotions, and a mixture of emotions & other features. • Usage: how emotions are used in CARS? E.g. which emotions are selected? which emotions are most influential ones? which components emotions are applied to? any latent patterns in terms of usage?
  • 14. • Recommender Systems (RS) & Affective RS • Context-aware Recommender Systems (CARS) • The Role of Emotions in CARS • Explore The Roles by CARS Algorithms • Experimental Results • Conclusions and Future Work
  • 15. Explore The Role of Emotions in CARS • Effectiveness: how effective the emotions are? Comparison among emotion only, no emotions, and a mixture of emotions & other features. • Usage: how emotions are used in CARS? E.g. which emotions are selected? which emotions are most influential ones? which components emotions are applied to? any latent patterns in terms of usage? • How to explore? Two classes of CARS algorithms: Context-aware Splitting Approaches; Differential Context Modeling; Baseline: Context-aware Matrix Factorization
  • 16. Context-aware Splitting Approaches • Three Algorithms: Item Splitting, User Splitting, UI Splitting • Item Splitting: The underlying idea is that the nature of an item, from the user's point of view, may change in different contextual conditions, hence it may be useful to consider it as two different items. (L. Baltrunas, F. Ricci, RecSys'09) At Cinema At Home At Swimming Pool
  • 17. Context-aware Splitting Approaches Splitting Process: Choose a contextual condition to split each item; The selection process is done by measuring significance of rating differences (such as the two-sample t test); Then multidimensional matrix is converted to a 2D one, then traditional algorithms like CF, MF can be applied to; User Item Loc Rating U1 M1 Pool 5 U2 M1 Pool 5 U3 M1 Pool 5 U1 M1 Home 2 U4 M1 Home 3 U2 M1 Cinema 2 High Ratings Low Ratings Significant difference? Let’s split it !!!
  • 18. Context-aware Splitting Approaches Matrix Transformation User Item Loc Rating U1 M1 Pool 5 U2 M1 Pool 5 U3 M1 Pool 5 U1 M1 Home 2 U4 M1 Home 3 U2 M1 Cinema 2 User Item Rating U1 M11 5 U2 M11 5 U3 M11 5 U1 M12 2 U4 M12 3 U2 M12 2 Transformation An alternative contextual condition for splitting: “Pool” vs. “Non-Pool” If there is qualified split, one item will be split to two new ones.  Contexts are fused into items as new items in 2D rating matrix;  Traditional RS algorithms can be applied to;
  • 19. Context-aware Splitting Approaches • User Splitting: is a similar one. Instead of splitting items, it may be useful to consider one user as two different users, if user demonstrates significantly different preferences across contexts. (L. Baltrunas, et al., CARS@RecSys 2009 and A. Said et al., CARS@RecSys 2011) • UI Splitting: simply a combination of item splitting and user splitting – both approaches are applied to create a new rating matrix – new users and new items are created in the rating matrix. (Y. Zheng, R. Burke, B. Mobasher, Decisions@RecSys 2013)
  • 20. Context-aware Splitting Approaches
  • 21. Context-aware Splitting Approaches How about the characteristics of this approach in terms of discovering the usage of contexts in the recommendation process? Context-aware splitting approaches have to select influential contextual conditions to split users or items; so the usage can be inferred by the distribution of which emotional contexts are used to split items and users, and which ones are the most frequent used ones?
  • 22. Differential Context Modeling  Separate one algorithm to different components;  Apply optimal contextual constraints to each component. The underlying is that, one context may be influential for this component, but it is not usually the case for other components.
  • 23. Differential Context Modeling Contextual constraints can be applied by a condition-matching (differential context relaxation, DCR) or a similarity-matching (differential context weighting, DCW); In DCR, the contexts should exactly matched by selected variables. In DCW, a similar enough context is allowed – it is not necessary to be exactly matched, but should be similar enough. Location: Home, Cinema, Swimming Pool Time: Weekend, Weekday Companion: Family, Friend, Girlfriend If location and time are selected: DCR  exactly match {Cinema, Weekend} DCW{Home, Weekend} is 50% similar! (Simply, assume dimensions have equal weights in DCW)
  • 24. Differential Context Modeling How about the characteristics of this approach in terms of discovering the usage of contexts in the recommendation process? DCR  Which emotional dimensions are selected for each component? (feature selection) DCW  Which dimensions are weighted higher in each component? (feature weighting)
  • 25. Baseline in Performance: CAMF CAMF = Context-aware Matrix Factorization We just applied CAMF to compare predictive performance, and did not explore the usage of contexts in this approach. Basic MF: Bias MF: CAMF_CI: CAMF_CU: Reference: Baltrunas, Linas, Bernd Ludwig, and Francesco Ricci. "Matrix factorization techniques for context aware recommendation." Proceedings of the fifth ACM conference on Recommender systems. ACM, 2011. Three Shapes: CAMF_C, CAMF_CI, CAMF_CU
  • 26. • Recommender Systems (RS) & Affective RS • Context-aware Recommender Systems (CARS) • The Role of Emotions in CARS • Explore The Roles by CARS Algorithms • Experimental Results • Conclusions and Future Work
  • 27. Experimental Design Contextual variables in the Dataset: LDOS-CoMoDa Emotional contexts # of users: 113; # of items: 1186; # of ratings: 2094; rating scale: 1-5 Each user has at least 5 ratings profiles in the data;
  • 28. Experimental Design • Effectiveness: how effective the emotions are? Comparison among emotion only, no emotions, and a mixture of emotions & other features (all contexts). • Usage: how emotions are used in CARS? E.g. which emotions are selected? which emotions are most influential ones? which components emotions are applied to? any latent patterns in terms of usage?
  • 29. • Effectiveness: Comparison among emotion only, no emotions, and a mixture of emotions & other features (all contexts) by RMSE. Findings:  “No emotions” plays the worst; (Note: MF is applied to splitting approaches)  “Emotion Only” works the best for CAMF; “All contexts” is the best for other two approaches;  UI splitting achieves lowest RMSE in all situations: All contexts, no emotions & emotion only;  User splitting performs better than item splitting for this data;  Emotions are beneficial !!!
  • 30. Experimental Design • Effectiveness: how effective the emotions are? Comparison among emotion only, no emotions, and a mixture of emotions & other features (all contexts). • Usage: how emotions are used in CARS? E.g. which emotions are selected? which emotions are most influential ones? which components emotions are applied to? any latent patterns in terms of usage?  Context-aware splitting approaches the usage distribution of contexts used to split items or users  Differential context modeling DCR  Which dimensions are selected for each component? DCW  Which dimensions are weighted higher in each component?
  • 31. • Usage by Context-aware Splitting Approaches Top-3 selections in item splitting: EndEmotion, Time, Season Top-3 selections in user splitting: EndEmotion, DominantEmo, Time Note: User splitting outperforms item splitting, any patterns? Conjecture: dependences between contexts and users|items.
  • 32. Experimental Results • Usage by Differential Context Modeling Notice: we applied DCM to user-based Collaborative Filtering.  DCR  Which contexts are selected for each component?  DCW  Which contexts are weighted higher in each component? For DCW, we did not show weights, but give selected dimensions weighted higher than 0.7. In DCR, emotional variables are selected in components of user baseline and similarity; In DCW, emotions are weighted significantly higher expect for neighbor contribution; DCM is optimal algorithm in context selection and weighting. Once emotions are selected or weighted highly, they are optimal solutions!
  • 33. • Summary of the Usage Patterns In Context-aware Splitting Approaches:  EndEmo and DominantEmo are two most influential ones!  And emotion variables defeat other variables! (topper selection!)  User splitting works better than Item splitting for this data In Differential Context Modeling:  EndEmo and DominantEmo are selected in DCR, and all three emotions are selected in DCW.  EndEmo and DominantEmo are influential for two components in both DCR and DCW: user baseline and user similarity calculation Overlapped findings: EndEmo and DominantEmo are influential ones!
  • 34. • Recommender Systems (RS) & Affective RS • Context-aware Recommender Systems (CARS) • The Role of Emotions in CARS • Explore The Roles by CARS Algorithms • Experimental Results • Conclusions and Future Work
  • 35. • What we did? Emotions are demonstrated to improve recommendation performance, but few research focus on the other side of the roles of emotions – the usage instead of performance only. In this work, we explore the roles (effectiveness + usage) by two classes of CARS algorithm: context- aware splitting approach, and differential context modeling • What we found? Effectiveness: UI splitting performs the best among all CARS algorithm for this data set; A mixture of all contexts is better for splitting approaches and DCM; CAMF may require context selection in advance; Usage: Splitting methods can explore usage by distribution of how frequent one context is used to split items or users; DCM infers the selection or the degree of importance by weights for each algorithm component; It can tell the importance of contexts by algorithm components. Patterns: Among three emotional variables, DominantEmo & EndEmo are two influential ones; Conjecture: User splitting may outperform item splitting if contexts are more dependent with users than items; experiments on more data are required to confirm this pattern.
  • 36. Future Work  The emotions in this data are not real emotions, e.g. DominantEmo and EndEmo are post-emotions, how about pre-emotions (emotion before the activity)?  What are the insight of those emotions? Any associations among emotions, user profiles and item features? For example, EndEmo is sad after seeing a tragedy, but rating is high (because tragedy makes people cry…), Or, a happy EndEmo will always results in higher rating? (because users likes this experience).  In terms of the three splitting approaches, especially the new proposed one – UI Splitting, it is useful to perform an empirical study among those three splitting approaches to further explore in which situation, which one will perform better than other ones.
  • 37. References Item Splitting 1) L. Baltrunas, and F. Ricci. "Context-based splitting of item ratings in collaborative filtering." RecSys, 2009. 2) L. Baltrunas, and F. Ricci. "Experimental evaluation of context-dependent collaborative filtering using item splitting." User Modeling and User-Adapted Interaction (2013): 1-28. User Splitting 1) L. Batrunas and X. Amatriain."Towards Time-Dependent Recommendation Based on Implicit Feedback." CARS@RecSys, 2009 2) A. Said, E. Luca, S. Albayrak. "Inferring contextual user profiles—improving recommender performance.“ CARS@RecSys, 2011 UI Splitting 1) Y. Zheng, R. Burke, B. Mobasher. "The Role of Emotions in Context-aware Recommendation". Decisons@RecSys, 2013 Context-aware Matrix Factorization 1) L. Baltrunas, B. Ludwig, F. Ricci. "Matrix factorization techniques for context aware recommendation." RecSys 2011. Differential Context Modeling 1) Y. Zheng, R. Burke, B. Mobasher. "Differential Context Relaxation for Context-aware Travel Recommendation". EC-WEB, 2012 2) Y. Zheng, R. Burke, B. Mobasher. "Optimal Feature Selection for Context-Aware Recommendation using Differential Relaxation". CARS@RecSys, 2012 3) Y. Zheng, R. Burke, B. Mobasher. "Recommendation with Differential Context Weighting". In UMAP, 2013
  • 38. Thank You! Decisions@RecSys 2013, Hong Kong Center for Web Intelligence, DePaul University, USA 10/12/2013