1. Music Recommender Systems 超群 .com [email_address] http://www.fuchaoqun.com 2009.11.22 Beta 技术沙龙 官方 twitter ： @betasalon 官方网站： http://club.blogbeta.com 邮件组： http://groups.google.com/group/betasalon?hl=zh-CN

2. Music Recommender Systems 超群 .com [email_address] http:// www.fuchaoqun.com

3. Who is Using Recommender Systems?

7. Association Rules TID Items 1 Bread 、 Milk 2 Bread 、 Diaper 、 Beer 、 Egg 3 Diaper 、 Beer 、 Cola 4 Bread 、 Milk 、 Diaper 、 Beer 5 Bread 、 Milk 、 Diaper 、 Cola Items Times Beer 、 Diaper 3 Bread 、 Milk 3 Beer 、 Bread 2 Diaper 、 Milk 2 Beer 、 Milk 1

10. Slope One User That is it Straight Through My Heart Jim 4 5 Mike 2 4 Fred 3 ？

13. Similarity Similarity ：

14. SVD Image copy from Here

15. SVD In Image Compression Original K=10 K=20

17. Demo from Here Which two people have the most similar tastes? Which two season are the most close?

18. Demo

19. Demo

21. MAGIC DIVISI ！ #!/usr/bin/env python #coding=utf-8 import divisi from divisi.cnet import * data = divisi.SparseLabeledTensor(ndim = 2) # read some rating into data # data[user_id, song_id] = 4 svd_result = data.svd(k = 128) # get songs that the user may like # predict_features(svd_result, user_id).top_items(100) # get similar songs # feature_similarity(svd_result, song_id).top_items(100) # get users that have similar tastes # concept_similarity(svd_result, user_id).top_items(100)

25. Data Preprocessing UserId SongId Times 3306 3654 200 3306 6950 236 3306 6528 268 3306 5874 325 3306 9527 126 3306 5624 98 3306 9635 115 3306 6962 210 … . … . … . UserId SongId Weight 3306 3654 0.62 3306 6950 0.73 3306 6528 0.82 3306 5874 1 3306 9527 0.39 3306 5624 0.30 3306 9635 0.35 3306 6962 0.65 … . … . … .

26. Data Mining UserId SongId Weight 3306 3654 0.62 3306 6950 0.73 3306 6528 0.82 3306 5874 1 3306 9527 0.39 3306 5624 0.30 3306 9635 0.35 3306 6962 0.65 … . … . … . UserId Similary Users’ Id … . … . SongId Similary Songs’ Id … . … .

28. That's it, Thanks. Q&A