Proceedings of the 16th IEEE/WIC/ACM International Conference on Web Intelligence (WI'17), Leipzig, Germany, August 2017

1. Affective Prediction By Collaborative
Chains In Movie Recommendation
Yong Zheng
School of Applied Technology
Illinois Institute of Technology
Chicago, IL, 60616, USA
The 2017 IEEE/WIC/ACM Conference on Web Intelligence (WI)
August 23-26, 2017, Leipzig, Germany

2. Agenda
• Background and Introduction
– Context-aware Recommender Systems
– Emotions In Recommender Systems
• Research Problems
– Emotion Acquisition
– Affective Predictions
• Methodologies and Results
• Conclusions and Future Work
2

3. Agenda
• Background and Introduction
– Context-aware Recommender Systems
– Emotions In Recommender Systems
• Research Problems
– Emotion Acquisition
– Affective Predictions
• Methodologies and Results
• Conclusions and Future Work
3

4. Recommender System (RS)
• RS: item recommendations tailored to user tastes
4

5. Context-Aware Recommendation
5
Companion
User’s decision may vary from contexts to contexts
• Examples:
➢ Travel destination: in winter vs in summer
➢ Movie watching: with children vs with partner
➢ Restaurant: quick lunch vs business dinner
➢ Music: for workout vs for study

6. Terminology in CARS
6
• Example of Multi-dimensional Context-aware Data set
➢Context Dimension: time, location, companion
➢Context Condition: Weekend/Weekday, Home/Cinema
➢Context Situation: {Weekend, Home, Kids}
User Item Rating Time Location Companion
U1 T1 3 Weekend Home Kids
U1 T2 5 Weekday Home Partner
U2 T2 2 Weekend Cinema Partner
U2 T3 3 Weekday Cinema Family
U1 T3 ? Weekend Cinema Kids

7. What is Context?
7
The most common contextual variables:
➢Time and Location
➢User intent or purpose
➢User emotional states
➢Devices
➢Topics of interests, e.g., apple vs. Apple
➢Others: companion, weather, budget, etc
Usually, the selection/definition of contexts is a domain-specific problem

8. Emotions and Emotional Effects
8

9. Incorporate Emotional Effects into RecSys
9
• Marko Tkalcic, Andrej Kosir, and Jurij Tasic. 2011. Affective recommender
systems: the role of emotions in recommender systems. In Proc. The RecSys
2011 Workshop on Human Decision Making in Recommender Systems. ACM, 9–
13
• Ante Odic, Marko Tkalcic, Jurij F Tasic, and Andrej Košir. 2012. Relevant context
in a movie recommender system: Users' opinion vs. statistical detection. ACM
RecSys 12 (2012)
• Yue Shi, Martha Larson, and Alan Hanjalic. 2013. Mining contextual movie
similarity with matrix factorization for context-aware recommendation. ACM
Transactions on Intelligent Systems and Technology (TIST) 4, 1 (2013), 16.
• Yong Zheng, Bamshad Mobasher, and Robin Burke. 2016. Emotions in context-
aware recommender systems. In Emotions and Personality in Personalized
Services. Springer, 311–326
• Yong Zheng. 2016. Adapt to Emotional Reactions In Context-aware
Personalization. In 4th Workshop on Emotions and Personality in Personalized
Systems (EMPIRE) 2016 co-located with ACM RecSys 2016

10. Agenda
• Background and Introduction
– Context-aware Recommender Systems
– Emotions In Recommender Systems
• Research Problems
– Emotion Acquisition
– Affective Predictions
• Methodologies and Results
• Conclusions and Future Work
10

11. Emotion Acquisition
11
We can collect emotions
➢By user surveys
➢By special user interactions, such as emoji
➢By Emotion Recognition or Extraction, e.g., from
texts, voice, facial expressions, etc
➢By Affective Prediction – a learning process to
predict emotional states from limited knowledge at
hand

12. Challenges in Affective Prediction
12
Emotional expression may happen in different stages

13. Challenges in Affective Prediction
13
There are correlations between emotions in two stages. For
example, a user may feel sad before watching a movie. He may
be dissatisfied with the movie and leave a negative reaction after
the movie watching

14. Research Problems
14
We focus on the following problems:
➢How to better predict affective states
➢How to take emotion correlations into account

15. Agenda
• Background and Introduction
– Context-aware Recommender Systems
– Emotions In Recommender Systems
• Research Problems
– Emotion Acquisition
– Affective Predictions
• Methodologies and Results
• Conclusions and Future Work
15

16. LDOS-CoMoDa Movie Data Set
16
There are 2291 ratings given by 121 users on 1232
movies. There are 12 contextual dimensions

17. 1. Independent Emotion Classification (IEC)
17
The problem is viewed as a classification problem
➢Features: user info and item features
➢Label(s): emotional variables
We use a binary classification algorithm to predict
the binary value for each emotional variable
independently.

18. 2. Dependent Emotion Classification (DEC)
18
For example, Classification Chains
➢Features: user info and item features
➢Label(s): emotional variables

19. 3. Independent Collaborative Prediction (ICP)
19
We choose collaborative filtering as the predictive
model, since it may work better on personalization
than the classification.
We select one-class matrix factorization with side
information as the model in our experiments.
• Yi Fang and Luo Si. 2011. Matrix co-factorization for
recommendation with rich side information and implicit feedback.
In Proceedings of the 2nd Workshop on Information Heterogeneity
and Fusion in Recommender Systems. ACM, 65–69

20. 4. Dependent Collaborative Chains (DCC)
20
We select one-class matrix factorization with side
information as the model in our experiments.

21. Experimental Settings
21
➢We use the LDOS-CoMoDa movie rating data
➢5-fold cross validation is applied
➢We predict the emotions for the test set first, and
examine the accuracy of the predictions
➢The predicted emotions will be incorporated into one
context-aware recommendation models to examine the
quality of context-aware recommendations.

22. Quality of the Affective Predictions
22

23. Quality of the Context-aware Recommendations
23
• Yong Zheng. 2016. Adapt to Emotional Reactions In Context-aware
Personalization. In 4th Workshop on Emotions and Personality in Personalized
Systems (EMPIRE) 2016 co-located with ACM RecSys 2016 [ the
recommendation model used in the paper]
• Actual  the performance when we use the actual emotions
• Predicted  the performance when we use the predicted emotions

24. Agenda
• Background and Introduction
– Context-aware Recommender Systems
– Emotions In Recommender Systems
• Research Problems
– Emotion Acquisition
– Affective Predictions
• Methodologies and Results
• Conclusions and Future Work
24

25. Conclusions
25
➢We explore the affective predictions
➢We predict the emotions by classification and collaborative
filtering respectively
➢For each solution, we figure out a way to incorporate
correlations among emotions
➢Collaborative predictions can help improve the quality of
personalizations
➢The dependent collaborative chains is demonstrated as the
best predictive model
➢The predicted emotional states can also help obtain good
context-aware recommendations.

26. Future Work
26
➢We plan to evaluate the proposed models in other
domains rather than the movie domain only
➢The problem of affective prediction is closely related to
a novel research topic – context suggestion, where we
predict or recommend appropriate contexts to the end
users.
➢In our future work, we will try to utilize the context
suggestion as solutions to help predict the emotional
states

27. Affective Prediction By Collaborative
Chains In Movie Recommendation
Yong Zheng
School of Applied Technology
Illinois Institute of Technology
Chicago, IL, 60616, USA
The 2017 IEEE/WIC/ACM Conference on Web Intelligence (WI)
August 23-26, 2017, Leipzig, Germany