1. Using Personality Information in Collaborative Filtering for New Usersrong Hu, Pearl PuProceedings of the 2nd ACM RecSys’10 Workshop on Recommender Systems and the Social Web Vincent Chu2010/10/6

3. Collaborative Filtering (CF) method

4. Cold Start Problem

5. Personality-based Collaborative Filtering

6. Personality Model

7. Personality-based Similarity Measure

8. Empirical Analysis

9. Evaluation Metrics

10. Experiment Results

11. Conclusion2

12. Collaborative Filtering (CF) method 3

14. Personality-based Collaborative Filtering 5

16. Openness to Experience 嘗試精神

17. Conscientiousness 盡責性

18. Extroversion 外向程度

19. Agreeableness 合群度

20. Neuroticism 情緒穩定度6

22. Each item consists of two descriptions separated by a comma.

23. Each item was rated on a 7-point scale rating for 1(Strongly disagree) to 7(Strongly agree).

24. Ex. I see myself1. ( ) Extraverted , Enthusiastic 2. ( ) Critical, Quarrelsome 7

26. Original Pearson correlation

27. Modified Pearson correlation8

28. Personality-based Collaborative Filtering 9 simr(u,v)=2−41−3.3+4−45−3.3+(6−4)(4−3.3)(2−4)2+(4−4)2+(6−4)2∗(1−3.3)2+(5−3.3)2+(4−3.3)2=68.39=0.715 Let γ =2 , simr'(u,v)=min(3,2)2∗ simr(u,v)=0.715

30. Personality Descriptor

31. Personality Pearson correlation

32. Personality-based Collaborative Filtering10

33. Personality-based Collaborative Filtering 11 simp(u,v)=−0.1 Let α =0.5 , simu,v=0.5∗simr′(u,v)+0.5∗ simp(u,v)=0.3075

35. Mean absolute error(MAE)

36. Decision-Support Accuracy Metrics

37. Recall & Specificity12

39. Influence of Model Size 14

40. Influence of Weight 15

41. Influence of Training Size 16

43. Dataset is too small

44. Didn’t evaluate the performance of each similarity measure using a dataset whose user-item matrix has a higher density

45. Future work17