Using personality information in collaborative filtering for new User
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Using personality information in collaborative filtering for new User
- 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
- 11. Conclusion2
- 12. Collaborative Filtering (CF) method 3
- 16. Openness to Experience 嘗試精神
- 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
- 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
- 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
- 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
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- Example graph/chart.
- Example graph/chart.
- Objectives for instruction and expected results and/or skills developed from learning.