Abstract
Collaborative filtering (CF) aims to recommend items based on prior user interaction. Despite their success, CF techniques do not handle data sparsity well, especially in the case of the cold start problem where there is no past rating for an item. In this paper, we provide a framework, which is able to tackle such issues by considering item-related emotions and semantic data. In order to predict the rating of an item for a given user, this framework relies on an extension of Latent Dirichlet Allocation, and on gradient boosted trees for the final prediction. We apply this framework to movie recommendation and consider two emotion spaces extracted from the movie plot summary and the reviews, and three semantic spaces: actor, director, and genre. Experiments with the 100K and 1M MovieLens datasets show that including emotion and semantic information significantly improves the accuracy of prediction and improves upon the state-of-the-art CF techniques. We also analyse the importance of each feature space and describe some uncovered latent groups.
Original language | English |
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Title of host publication | Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Place of Publication | New York |
Pages | 625-634 |
Number of pages | 10 |
DOIs | |
Publication status | Published - 24 Jul 2011 |
Event | 34th Annual ACM SIGIR Conference - Beijing, China Duration: 24 Jul 2011 → 28 Jul 2011 http://www.sigir2011.org/ |
Conference
Conference | 34th Annual ACM SIGIR Conference |
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Country/Territory | China |
City | Beijing |
Period | 24/07/11 → 28/07/11 |
Internet address |
Keywords
- collaborative filtering
- collaborative recommendation
- semantic
- emotion