Memory-based methods for collaborative filtering predict new ratings by averaging (weighted) ratings between, respectively, pairs of similar users or items. In practice, a large number of ratings from similar users or similar items are not available, due to the sparsity inherent to rating data. Cons
[ACM Press the 29th annual international ACM SIGIR conference - Seattle, Washington, USA (2006.08.06-2006.08.11)] Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '06 - Unifying user-based and item-based collaborative filtering approaches by similarity fusion
โ Scribed by Wang, Jun; de Vries, Arjen P.; Reinders, Marcel J. T.
- Book ID
- 118062799
- Publisher
- ACM Press
- Year
- 2006
- Tongue
- English
- Weight
- 217 KB
- Volume
- 0
- Category
- Article
- ISBN-13
- 9781595933690
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