Problems with user based collaborative filtering 1

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Problems with User-based Collaborative Filtering (1) User Cold-Start problem not enough known about new user to decide who is similar (and perhaps no other users yet..) Need way to motivate early rater
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Problems with User-based Collaborative Filtering (2) Sparsity when recommending from a large item set, users will have rated only some of the items (makes it hard to find similar users)
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Problems with User-based Collaborative Filtering (3) Scalability with millions of ratings, computations become slow Item Cold-Start problem Cannot predict ratings for new item till some similar users have rated it [No problem for content-based]
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Demographic Recommenders To predict a user’s opinion for an item, use the opinion of similar users [as in user-based Collaborative Filtering] But, similarity between users is decided by looking at demographics (stereotypes) Otherwise, default to all users Most popular lists, etc.
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Item-based Collaborative Filtering User is likely to have the same opinion for similar items [same idea as in Content-Based Filtering] Similarity between items is decided by looking at how other users have rated them [different from Content-based, where item features are used] Star Wars = [Action, Sci-fi...] Star Wars = [User1:8, User2:3, User3:7...] Advantage (compared to user-based CF): Prevents User Cold-Start problem Improves scalability (similarity between items is more stable than between users)
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Example: Item-based Collaborative Filtering Item 1 Item 2 Item 3 Item 4 Item 5 User 1 8 1 ? 2 7 User 2 2 ? 5 7 5 User 3 5 4 7 4 7 User 4 7 1 7 3 8 User 5 1 7 4 6 5 User 6 8 3 8 3 7
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Similarity between items Item 3 Item 4 Item 5 ? 2 7 5 7 5 7 4 7 7 3 8 4 6 5 8 3 7 How similar are items 3 and 4?
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  • Spring '16
  • Sameh
  • Cold start, Recommender system

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