1. Recommender System Wenxin Zhao 2014/04/04 CS548 Showcase Worcester Polytechnic Institute

2. Reference Papers Oscar Celma Herrada, "MUSIC RECOMMENDATION AND DISCOVERY IN THE LONG TAIL" 2008 Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl "Item-Based Collaborative Filtering Recommendation Algorithms" PPT Sue Yeon Syn‘s PPT "Collaborative filtering" --September 21, 2005 Web pages Collaborative-filtering: http://en.wikipedia.org/wiki/Collaborative_filtering

3. Music recommender Web application Heart means you like this song. More hearts you choose, there is higher possibility we can recommend songs you may like. Dump: dislike this song. Skip: Skip this song. Heart: like this song. From fm.baidu.com

4. potentially—interesting and unknown music, hidden in the tail of the popularity curve. Popularity Item From Oscar Celma Herrada, "MUSIC RECOMMENDATION AND DISCOVERY IN THE LONG TAIL" 2008

5. DATA Source 1.User dump records, heart records, skip records. 2.Other users dump records, heart records, skip records and recommended album or songs the recommender system has. It would give every item a rating based on the user records. User 1 User 2 User 3 User 4... Item 1 ratin g1.1 ratin g2.1 ？ Item 2 ratin g1.2 ？ Item 3....

6. Collaborative filtering(CF) Collaborative-filtering.gif: http://en.wikipedia.org/wiki/Collaborative_filtering Definition: The process of information filtering by collecting human judgments (ratings) User-based collaborative filtering Item-based collaborative filtering

7. Collaborative filtering(CF) From http://en.wikipedia.org/wi ki/Collaborative_filtering http://en.wikipedia.org/wi ki/Collaborative_filtering

8. Collaborative filtering(CF) From http://en.wikipedia.org/wi ki/Collaborative_filtering http://en.wikipedia.org/wi ki/Collaborative_filtering

9. Collaborative filtering(CF) From http://en.wikipedia.org/wi ki/Collaborative_filtering http://en.wikipedia.org/wi ki/Collaborative_filtering

10. Collaborative filtering(CF) similar users A B C D E From http://en.wikipedia.org/wi ki/Collaborative_filtering http://en.wikipedia.org/wi ki/Collaborative_filtering

11. Collaborative filtering(CF) A B C D E From http://en.wikipedia.org/wi ki/Collaborative_filtering http://en.wikipedia.org/wi ki/Collaborative_filtering

12. Collaborative filtering(CF) E From http://en.wikipedia.org/wi ki/Collaborative_filtering http://en.wikipedia.org/wi ki/Collaborative_filtering

13. User-based collaborative filtering User-Based Nearest Neighbor – Neighbor = similar users – Generate a prediction for an item i by analyzing ratings for i from users in u’s neighborhood From Sue Yeon Syn‘s PPT "Collaborative filtering" September 21, 2005

14. Item-based collaborative filtering Item-Based Nearest Neighbor – Generate predictions based on similarities between items. – Prediction for a user u and item i is composed of a weighted sum of the user u’s ratings for items most similar to i. From Sue Yeon Syn‘s PPT "Collaborative filtering" September 21, 2005

15. Problem Cold-start--new user Sparsity--transactional data are lacking or are insufficient.

16. Thank you ! Question ?