E-MRS: Emotion-based Movie Recommender System. Ai Thanh Ho, Ilusca L. L. Menezes, and Yousra Tagmouti

Transcription

1 E-MRS: Emotion-based Movie Recommender System Ai Thanh Ho, Ilusca L. L. Menezes, and Yousra Tagmouti IFT /12/2006

2 Outline Introduction and Future Work 2

3 Introduction Electronic commerce Large amount of information Too many products, especially subjective products difficult to choose Need of personalization Recommender system Recommend products based on user preferences need to create a separate profile for each user 3

4 Recommender Systems (Inspired from [ChoiceStream 2004]) 4

5 Recommendation Techniques [Burke 2002] 5

6 Recommender system User Profile: Created by voting, answering questionnaire Cannot capture exactly user current preferences Subjective products difficult to evaluate User preferences change continuously difficult to detect these changes Subjective products are closely related to user emotions 6

7 Our contribution: An Emotion-based Recommender System (E-MRS) Our objective: To recommend movies based on user emotions (Inspired from [ChoiceStream 2004]) 7

8 Why emotion is important? The relation between and Movies 8

9 Classification of Researchers do not agree neither on a list of basic emotions nor on the criteria that identify them Our work is based on the studies carried out by Parrot (2001) Six basic emotions: love, joy, surprise, anger, sadness and fear 9

10 Method of Emotion detection Sensor Questionnaire Colour Some researchers agreed that the colours have a strong impact on our emotions and feelings [Hemphill 1996; Mahnke 1996] Colour is a natural form to represent human emotions [Black 2002] 10

11 Yellow Black Green Blue Grey Brown Red Orange Light Colours Dark Colours Positive Joy, merry, good mood [Wexner 1954; Murray et al. 1957; Schaie 1961; Wexner 1982; Collar 1996; Ballast 2002] Happiness, calmness and feelings of relieving [Kaya et al. 2004] Pleasure and happiness [Schaie 1961; Kaya et al. 2004] Love, passion, excitation [Collar 1996; Pecjak 1970; Ballast 2002; Wexner 1982; Odbert et al. 1942; Murray et al ] Cheerfulness [Collar 1996; Murray et al. 1957; Schaie 1961] [Boyatzis et al. 1994; Hemphill 1996] Negative Sadness, depression and loneliness [Murray et al. 1957; Odbert et al. 1942; Collar 1996; Schaie 1961] Sadness, depression and loneliness [Murray et al. 1957; Odbert et al. 1942; Collar 1996; Schaie 1961]. Sadness and depression [Collar 1996] Anger [Schachtel, 1943; Pecjak 1970; Frasson et al ] Fear and distress [Ballast 2002; Wexner 1982; Murray et al.1957] [Boyatzis et al. 1994; Hemphill 1996] 11

12 Colour and Our work is based on these researches The summary of the relation between colours and emotions that will be used in our system 12

13 Recommend movies based on emotions Recommend movie by the rules no psychological rules linking emotion and movies To learn the rules by training users answer a questionnaire, indicating their movies preferences according to their emotions Our movie-emotion questionnaire 13

14 The E-MRS architecture 14

15 Sequence diagram 15

16 Screenshot of the User Interface 16

17 Screenshot 17

18 Evaluation Methodologies Mean absolute error (MAE): Measure how close the recommender system s predicted ratings are to the true user s ratings Best case: 0 (no difference between predicted ratings and user s ratings) Precision: The ratio between the number of well-recommended items and the total number of rated recommendations Good product recommendations were rated at least 4 out of 5 by users Bad recommendations were 1, 2 or 3 Best case: 100% (all recommendations are good) [Herlocker et al., 2004] 18

19 E-MRS has the novelty of incorporating user emotions into the user profile to provide users with well-recommended products based on their emotional state Users can give their feedback about how a recommended movie meets their preferences and this feedback improves the recommendation quality over time We believe that E-MRS with the Emotion detector can greatly improve the efficiency of the movie recommendation 19

20 Future work The relation between colours and emotions depends on culture, religion of users we shall consider these aspects The system implementation and validation Thank you! Questions? 20

21 References (1/4) Burke, R. Recommender Systems: Survey and Experiments, In User Modeling and User-Adapted Interaction. Vol 12. Kluwer Academic Publishers, 2002, pp ChoiceStream, Review of Personalization Technologies: Collaborative Filtering vs. ChoiceStream s Attributized Bayesian Choice Modeling, Technology Brief, Parrott, W. in Social Psychology. Psychology, Philadelphia, Oatley, K and Jenkins, J.M. Understanding : Blackwell, Cacioppo, J. T., Bernston, G. G., Larsen, J. T., Poehlmann, K. M., & Ito, T. A. The psychophysiology of emotion. New York: The Guilford Press, Parrott, W. in Social Psychology. Psychology, Philadelphia, Hemphill, M. A note on adults' color-emotion associations. Journal of Genetic Psychology,

22 References (2/4) Mahnke F. Color, environment, human response. New York: Van Nostrand Reinhold, Black, J. Color Psychology for E-book Cover Design Wexner. L.B. The degree to which colors are associated with mood-tones. J. appl. Psychol Murray, D. C., & Deabler, H.L. Colors and mood-tones. Journal of applied Psychology Schaie, K. W. Scaling the association between colors and mood tones. The American Journal of Psychology Wexner, L. B. The degree to which colors are associated with mood-tones. Journal of Applied Psychology

23 References (3/4) Collier, G. L. Affective synaesthesia: Extracting emotion space from simple perceptual stimuli. Motivation and Emotion Ballast, D. K. Interior design reference manual. Professional Pub. Inc.: Belmont, CA, Kaya, N. & Epps, H. Relationship between color and emotion: A study of college students. College Student Journal Odbert, H. S., Karwoski, T. F., & Eckerson, A. B. Studies in synesthetic thinking: I. Musical and verbal associations of color and mood. In The Journal of General Psychology pp 26, Herlocker, J. L. Konstan, J. A. Riedl, J. Explaining collaborative filtering recommendations, Proceedings of the 2000 ACM conference on Computer supported cooperative work,

24 References (4/4) Frasson, C., Chaffar, S., Abdel Razek M., Ochs M. Using Machine-Learning Techniques to Recognize for On-Line Learning. In The 18th International FLAIRS Conference, AAAI Press, Clearwater, FL, USA pp Pecjak, V. Verbal synesthesiae of colors, emotions, and days of the week. In Journal of Verbal Learning and Verbal Behavior pp 9, Boyatzis, C. J., & Varghese, R. Children's emotional associations with colors. Journal of Genetic Psychology pp 155, Hill, W., Stead, L., Rosenstein, M., And Furnas, G. W Recommending and evaluating choices in a virtual community of use. In Proceedings of ACM CHI 95 Conference on Human Factors in Computing Systems. 1995, pp

25 Emotion Detection Algorithm Legend: 25

26 Collaborative Filtering [ChoiceStream 2004] 26

27 Recommendation Algorithm: Collaborative filtering Step 1: Construct the profile vector of the target user u Step 2: Search for other users who have rated at least one movie in common with the target user u Step 3: Compute the correlation between the profile vector of target user u and each of the other members of his neighbourhood Step 4: Calculate the predicted rating of target user u for movie j 27

28 Content-based Filtering 28 (Inspired from [ChoiceStream 2004])

29 Recommendation Algorithm: Content-based filtering 29

30 Validation Procedure Evaluating recommender systems and recommendation algorithms is very difficult Users do not provide consistent ratings when asked to rate the same movie at different times [Hill et al. 1995] 30

31 Evaluation Methodologies Mean absolute error (MAE): Measure how close the recommender system s predicted ratings are to the true user s ratings Best case: 0 (no difference between predicted ratings and user s ratings) Precision: The ratio between the number of well-recommended items and the total number of rated recommendations Good product recommendations were rated at least 4 out of 5 by users Bad recommendations were 1, 2 or 3 Best case: 100% (all recommendations are good) [Herlocker et al., 2004] 31

32 Comparison with other systems 32