Collaborative filtering
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Collaborative filtering is a technique used in recommender systems to predict a user's preferences based on other similar users' preferences. It involves collecting ratings or preference data from users, calculating similarities between users or items, and generating predictions for a user's unknown ratings based on weighted averages of the ratings from similar users or items. There are two main types: user-based which computes similarities between users, and item-based which computes similarities between items. Challenges include cold start problems, sparsity of data, scalability issues for large datasets, and reducing user bias.
![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](https://image.slidesharecdn.com/collaborativefiltering-150228122057-conversion-gate02/85/Collaborative-filtering-6-320.jpg)
Collaborative filtering
- 1. Collaborative Filtering is a technique used by some recommender systems NCKU-hpds TienYang
- 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. when we choose one book，amazon will recommend other book we maybe like
- 4. how amazon know what I like?
- 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. 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. 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. 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. ✤ User-Based CF ✤ Item-Based CF compute similarity base on user compute similarity base on item
- 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. ✤ 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. similarity!? Cosine Similarity Pearson Correlation Similarity how about ? (1,-1)
- 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. what is different between ? Pearson Correlation SimilarityCosine Similarity AWS: lower user bias!
- 16. what is different between Pearson Correlation Similarity Cosine Similarity Adjusted Cosine Similarity advanced average user rating average item rating
- 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. 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. 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. Collaborative Filtering Gist Collaborative Filtering ipynb online Scaling-up Item-based Collaborative Filtering Recommendation Algorithm based on Hadoop PPT code and PPT
- 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