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Collaborative Filtering

Collaborative Filtering. What is collaborative filtering (CF) ?. The most popular technique for recommender system Making filtering decisions for an individual user based on the judgments of other users General Idea Given an active user u, find friends {u 1 , … , u m }

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Collaborative Filtering

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  1. Collaborative Filtering

  2. What is collaborative filtering (CF) ? • The most popular technique for recommender system • Making filtering decisions for an individual user based on the judgments of other users • General Idea • Given an active user u, find friends {u1, … , um} • Recommend new items to the active user based on the opinions of his friends

  3. What is collaborative filtering (CF) ?

  4. Collaborative Filtering Matrix 2 3.5 4 1.5

  5. CF : Intuitions • User similarity • Suppose Jamie and I view similar movies in the past six months … • If Jamie watched “Star Wars”, I will also like it • Item similarity • Since 90% of those who liked “Star Wars” also liked “Independence Day”, and, you liked “star Wars” • You may also like “Independence Day”

  6. Short History of CF • 1992 • GroupLens Project : (Usernet News) 1994 • 1995 • Ringo: music (later Firefly, purchased by Microsoft?) • Bellcore: Video Recommender • 1996 • Recommender Systems Workshop in SIGIR • After 1996 • substantial integration with machine learning, information filtering • Increasing commercial application

  7. Commercial applications –amazon.com • www.amazon.com • Input: One artist/author name

  8. Commercial applications –Amazon.com Search using Recommendations • Output: List of Recommendations • Explore / Refine Recommendations

  9. Commercial applications –Sleeper • Input: Ratings of 10 books for all users • Use of continuous Rating Bar

  10. Commercial applications - Alexa http://www.alexa.com

  11. Commercial applications- Movielens www.movielens.umn.edu

  12. Category of CF • Memory-based approaches • Earlier techniques • Adapted by most commercial websites • Example: Pearson CF Algorithm • Model-based approaches • Hot topic for recent research • More accurate , fast and complicated • Example: Bayesian Clustering Model, Aspect Model

  13. Memory-based Approaches • General Ideas for user-based • Find top K friends for active user a based on user similarity- Sim (a, i) • Make prediction for the active user based on the ratings of those top K friends, Pre(a,i) = f( ratings of K users, i) • Specific approaches differ in • Sim(a,i) -- the distance/similarity between two users • Such as Pearson correlation coefficient and Cosine similarity • Many other possibilities • Pre(a,i) = f( ratings of K users, i)

  14. Step 1 : Find K nearest friends by Pearson CoefficientF Algorithm

  15. Step 2 : Predict user’s rating on item

  16. Pearson CF Algorithm • Pearson correlation coefficient So use the same method we can get Wken-meg=+1.0 . We do not needcalculate Wken-nab,because Nan do not gave mark for Music six.

  17. Pearson CF Algorithm • Predict Ken’s preference on Music 6

  18. Problem of Pearson CF • Data sparseness • Misleading friends

  19. Cluster-based Solution

  20. 评价值 物品-属性聚类 B.M. Kim, Q. Li, C. S. Park, S. Kim.  "A New Approach for Combining Content-based and Collaborative Filters", Journal of Intelligence Information Systems, Kluwer Academic Publishers (JIIS Vol. 21:1,July,2006). Q. Li,B.M. Kim,G. D. Hai,D.H Oh. “A Collaborative Music Recommender based on Audio Features”, In the Proc. of SIGIR-04, 2004, Sheffield, UK.

  21. Reference • Thomas Hofmann, Latent Semantic Models for Collaborative Filtering, ACM transactions on Information Systems Vol.22, No.1. 2004 • John S. Breese, David Heckerman Carl Kadie, Empirical Analysis of Predictive Algorithms for Collaborative Filtering Technical Report MSR-TR-98-12, 1998 • http://www.cs.umn.edu/Research/GroupLens/index.html • J. Bilmes. A Gentle Tutorial on the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models ICSI-TR-97-021, 1997.

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