Handling data sparsity in collaborative filtering using emotion and semantic based features

Yashar Moshfeghi, Benjamin Piwowarski, Joemon M. Jose

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

83 Citations (Scopus)


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 languageEnglish
Title of host publicationProceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of PublicationNew York
Number of pages10
Publication statusPublished - 24 Jul 2011
Event34th Annual ACM SIGIR Conference - Beijing, China
Duration: 24 Jul 201128 Jul 2011


Conference34th Annual ACM SIGIR Conference
Internet address


  • collaborative filtering
  • collaborative recommendation
  • semantic
  • emotion


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