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Collaborative Filtering
2019-02-17 01:40:32
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MachineLearning
### user-based collaborative filtering 1. for each user, find similar users by calculating similarity of the ratings (e.g. euclidean distance, pearson similarity) 2. for each item of the seleted users, calculate the weighted rating according to each user's similarity 3. select top n new items for this user ### item-based collaborative filtering 1. for each item, calculate similarity of each other item 2. select top rating items of this user 3. for each selected item, find similar items and calculate the weighted rating according to each item's similarity 4. select top n new items for this user ### user-based or item-based? - item-based method needs to maintain the item similarity table - for sparse dataset, item-based method is better - for dense dataset, both methods have the similar performance
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