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Recommender system

Recommender system. Taking 2 papers in WWW2012 for examples Xie Yanan. Recommender System. Recommender System. Collaborative Filtering (CF). …. 1. 2. User A. User B. 3. 4. 5. 6. …. Naïve Item-based CF. Naïve User-based CF. Paper 1.

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Recommender system

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  1. Recommender system Taking 2 papers in WWW2012 for examplesXieYanan

  2. Recommender System

  3. Recommender System

  4. Collaborative Filtering (CF) … 1 2 User A User B 3 4 5 6 …

  5. Naïve Item-based CF

  6. Naïve User-based CF

  7. Paper 1 • Build Your Own Music Recommender by Modeling Internet Radio Streams • Advantages • Freshness • Completeness • Robustness • Scale • Diversity • Accessibility

  8. Model • Maps both items and stations to latent factor vectors, a representation proven successful in many recommendation systems.

  9. Results

  10. Characteristics and Challenges ofCollaborative Filtering • Data Sparsity • cold start • Neighbor transitivity • Scalability • Synonymy • Gray Sheep • Shilling Attacks

  11. Paper 2: • An Exploration of Improving Collaborative Recommender Systems via User-Item Subgroups

  12. Multiclass Co-Clustering(MCoC)

  13. Problem Formulation • n users m items • c subgroups

  14. n users m items • c subgroups (our goal) Loss function

  15. Loss function

  16. Loss function

  17. Loss function • Notice r<c

  18. Fuzzy c-means

  19. Evaluation

  20. Thank you!

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