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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 Taking 2 papers in WWW2012 for examplesXieYanan
Collaborative Filtering (CF) … 1 2 User A User B 3 4 5 6 …
Paper 1 • Build Your Own Music Recommender by Modeling Internet Radio Streams • Advantages • Freshness • Completeness • Robustness • Scale • Diversity • Accessibility
Model • Maps both items and stations to latent factor vectors, a representation proven successful in many recommendation systems.
Characteristics and Challenges ofCollaborative Filtering • Data Sparsity • cold start • Neighbor transitivity • Scalability • Synonymy • Gray Sheep • Shilling Attacks
Paper 2: • An Exploration of Improving Collaborative Recommender Systems via User-Item Subgroups
Problem Formulation • n users m items • c subgroups
n users m items • c subgroups (our goal) Loss function
Loss function • Notice r<c