| 摘要 |
A method for recommending items to users using automated collaborative filtering stores profiles of users relating ratings to items in memory. Profiles of items are also stored in memory, the item profiles associating users with the rating given to the item by that user. Similarity factors with respect to other users are calculated for a user, and these similarity factors are used to select a set of neighboring users. The neighboring users are weighted based on their respective similarity factors, and a rating for an item contained in the domain is predicted. In one embodiment, items in the domain have features. In this embodiment, the values for features can be clustered, and the similarity factors incorporate assigned feature weights and feature value cluster weights. <IMAGE> |
