| 摘要 |
The present invention relates to an apparatus, method, and computer program product for controlling a recommender system, wherein user actions on content items are associated to explicit ratings on these content items and translated into a UI features profile, which is subsequently used by a recommender. This recommender rates new items based on user actions on this item and thus creates an implicitly learned rating history. This learning makes the implicit rating or scoring personalized. It can be combined in several ways with an explicitly learned rating history to improve overall performance and/or to mitigate the burden for the user, by having him/her rate less items explicitly. |
