The document discusses the role of emotions in context-aware recommender systems (CARS). It explores two classes of CARS algorithms: context-aware splitting approaches and differential context modeling. For context-aware splitting approaches, it examines which emotional contexts are most frequently used to split items or users. For differential context modeling, it analyzes which emotional dimensions are selected or weighted most highly for different algorithm components. The experimental results found that the emotions of end emotion and dominant emotion were the most influential across approaches. User splitting also generally outperformed item splitting.

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