Moshfeghi, Y., Agarwal, D., Piwowarski, B. and Jose, J. M. (2009) Movie recommender: semantically enriched unified relevance model for rating prediction in collaborative filtering. Lecture Notes in Computer Science, 5478, pp. 54-65. (doi: 10.1007/978-3-642-00958-7_8)
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Abstract
Collaborative recommender systems aim to recommend items to a user based on the information gathered from other users who have similar interests. The current state-of-the-art systems fail to consider the underlying semantics involved when rating an item. This in turn contributes to many false recommendations. These models hinder the possibility of explaining why a user has a particular interest or why a user likes a particular item. In this paper, we develop an approach incorporating the underlying semantics involved in the rating. Experiments on a movie database show that this improves the accuracy of the model.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Jose, Professor Joemon and Moshfeghi, Dr Yashar and Piwowarski, Dr Benjamin |
| Authors: | Moshfeghi, Y., Agarwal, D., Piwowarski, B., and Jose, J. M. |
| College/School: | College of Science and Engineering > School of Computing Science |
| Journal Name: | Lecture Notes in Computer Science |
| Publisher: | Springer |
| ISSN: | 0302-9743 |
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