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# Collaborative filtering

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Collaborative filtering

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### Collaborative filtering

1. 1. Collaborative Filtering is a technique used by some recommender systems NCKU-hpds TienYang
2. 2. E-Commerce Collaborative ﬁltering is a method of making automatic predictions (ﬁltering) about the interests of a user by collecting preferences or taste information from many users (collaborating). from wiki
3. 3. when we choose one book，amazon will recommend other book we maybe like
4. 4. how amazon know what I like?
5. 5. 1. Weight all users with respect to similarity with active user 2. Select a subset of users to use as a set of predictors 3. Compute a prediction from a weighted combination of selected neighbors’ ratings
6. 6. 1. Weight all users with respect to similarity with active user simple compute Nathan [5,1,5] Joe [5,2,5] John [2,5,2.5] Al [2,2,4] use cosine compute similarity cos (Nathan,Joe) 0.99 cos (Nathan,John) 0.64 cos (Nathan,Al) 0.91
7. 7. 1. Weight all users with respect to similarity with active user 2. Select a subset of users to use as a set of predictors if there are hundreds of user, we can choose the higher similarity choose n of m(sum of user is m)
8. 8. 1. Weight all users with respect to similarity with active user cos (Nathan,Joe) 0.99 cos (Nathan,John) 0.64 cos (Nathan,Al) 0.91 ? = 3.03 2. Select a subset of users to use as a set of predictors 3. Compute a prediction from a weighted combination of selected neighbors’ ratings (0.99*4+0.64*3+0.91*2) (0.99+0.64+0.91) 0.99 0.64 0.91
9. 9. ✤ User-Based CF ✤ Item-Based CF compute similarity base on user compute similarity base on item
10. 10. ✤ User-Based CF compute similarity base on user if predict user A to item4 rating user B to item4 rating is 5 user F to item4 rating is 1 user A to item4 = 5 * similarities (user A, user B) + 1 * similarities (user A, user F) similarities (user A, user B) + similarities (user A, user F)
11. 11. ✤ Item-Based CF compute similarity base on item if predict user A to item4 rating user A to item2 rating is 1 user A to item3 rating is 2user A to item4 = 1 * similarities (item2, item4) + 2 * similarities (item3, item4) similarities (item2, item4) + similarities (item3, item4)
12. 12. similarity!? Cosine Similarity Pearson Correlation Similarity how about ? (1,-1)
13. 13. j Covariance Pearson Correlation Similarity (1,-1)
14. 14. apple milk toast sam 2 0 4 john 5 5 3 tim 2 4 ? u i j Ri = (2+5)/2 Rj = (4+3)/2 Pearson Correlation Similarity
15. 15. what is different between ? Pearson Correlation SimilarityCosine Similarity AWS: lower user bias!
16. 16. what is different between Pearson Correlation Similarity Cosine Similarity Adjusted Cosine Similarity advanced average user rating average item rating
17. 17. apple milk toast sam 2 0 4 john 5 5 3 tim 2 4 ? u i j 2 * similarities (apple, toast) + 4 * similarities (milk, toast) similarities (apple, toast) + similarities (milk, toast) ? =
18. 18. so 1. Weight all items with respect to similarity with active items 2. Select a subset of items to use as a set of predictors 3. Compute a prediction from a weighted combination of selected neighbors’ ratings choose n of m(sum of user is m)
19. 19. Collaborative Filtering problem ? Cold-start Sparsity Scalability ALS-Alternating Least Squares SVD-singular value decomposition Hybrid Recommendation Systems Scaling-Up Item-Based Collaborative Filtering Recommendation Algorithm Based on Hadoop
20. 20. Collaborative Filtering Gist Collaborative Filtering ipynb online Scaling-up Item-based Collaborative Filtering Recommendation Algorithm based on Hadoop PPT code and PPT
21. 21. reference Item-based collaborative ﬁltering Algorithm Collaborative ﬁltering wiki Pearson correlation coefﬁcient wiki 協同過濾法 (collaborative Filtering) 及相關概念