Yashar Moshfeghi
Benjamin Piwowarski
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.
- G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. TKDE'05, June 2005. Google Scholar
Digital Library
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR'03, 2003. Google ScholarDigital Library
- P. Ekman and H. Oster. Facial Expressions of Emotion. Annual Reviews in Psychology, 1979.Google Scholar
- J. H. Friedman. Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 2001.Google ScholarCross Ref
- A. B. Goldberg and X. Zhu. Seeing stars when there aren't many stars: graph-based semi-supervised learning for sentiment categorisation. TextGraphs-1, 2006. Google ScholarDigital Library
- M. Hancock-Beaulieu and S. Walker. An evaluation of automatic query expansion in an online library catalogue. J. Doc., 1992. Google ScholarDigital Library
- T. Hofmann. Collaborative filtering via gaussian probabilistic latent semantic analysis. SIGIR'03, 2003. Google ScholarDigital Library
- T. Hofmann. Latent semantic models for collaborative filtering. ACM TOIS, 2004. Google ScholarDigital Library
- C. Izard. The Psychology of Emotions. Plenum Publishing Corporation, 1991.Google ScholarCross Ref
- N. Jakob, S. H. Weber, M. C. Müller, and I. Gurevych. Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations. TSA '09, 2009. Google ScholarDigital Library
- C. Leung, S. Chan, and F. Chung. Integrating collaborative filtering and sentiment analysis: A rating inference approach. ECAI'06, 2006.Google Scholar
- H. Liu, H. Lieberman, and T. Selker. A model of textual affect sensing using real-world knowledge. IUI'03, 2003. Google ScholarDigital Library
- H. Liu, T. Selker, and H. Lieberman. Visualizing the affective structure of a text document. CHI'03, 2003. Google ScholarDigital Library
- B. Marlin. Modeling user rating profiles for collaborative filtering. NIPS'04, 2004.Google Scholar
- S. M. A. Masum, H. Prendinger, and M. Ishizuka. Sentiment assessment of text by analyzing linguistic features and contextual valence assignment. Applied Artificial Intelligence, 2008. Google ScholarDigital Library
- R. McGill, J. Tukey, and W. Larsen. Variations of box plots. American Statistician, 1978.Google Scholar
- P. Melville, R. J. Mooney, and R. Nagarajan. Content-boosted collaborative filtering for improved recommendations. AAAI'02, 2002. Google ScholarDigital Library
- H. Misra, O. Cappé, and F. Yvon. Using lda to detect semantically incoherent documents. CoNLL'08, 2008. Google ScholarDigital Library
- B. Mobasher, X. Jin, and Y. Zhou. Semantically Enhanced Collaborative Filtering on the Web. Lecture Notes in Computer Science, 2004.Google Scholar
- Y. Moshfeghi, D. Agarwal, B. Piwowarski, and J. M. Jose. Movie Recommender: Semantically Enriched Unified Relevance Model for Rating Prediction in Collaborative Filtering. ECIR'09, 2009. Google ScholarDigital Library
- Y. Moshfeghi and J. M. Jose. Role of emotional features in collaborative recommendation. ECIR'11, 2011. Google ScholarDigital Library
- A. Ortony, G. Clore, and A. Collins. The cognitive structure of emotions. Cambridge University Press, 1990.Google Scholar
- B. Pang and L. Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. ACL'05, 2005. Google ScholarDigital Library
- S.-T. Park and W. Chu. Pairwise preference regression for cold-start recommendation. RecSys'09, 2009. Google ScholarDigital Library
- D. Pennock, E. Horvitz, S. Lawrence, and C. Giles. Collaborative filtering by personality diagnosis: A hybrid memory-and model-based approach. UAI'00, 2000. Google ScholarDigital Library
- P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. CSCW'94, 1994. Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. WWW'01, 2001. Google ScholarDigital Library
- M. A. M. Shaikh, H. Prendinger, and M. Ishizuka. A Linguistic Interpretation of the OCC Emotion Model for Affect Sensing from Text. Affective Information Processing, 2009.Google ScholarCross Ref
- P. Winoto and T. Y. Tang. The role of user mood in movie recommendations. Expert Systems with Applications, 2010. Google ScholarDigital Library
- G.-R. Xue, C. Lin, Q. Yang, W. Xi, H.-J. Zeng, Y. Yu, and Z. Chen. Scalable collaborative filtering using cluster-based smoothing. SIGIR'05, 2005. Google ScholarDigital Library
- K. Yu, S. Zhu, J. Lafferty, and Y. Gong. Fast nonparametric matrix factorisation for large-scale collaborative filtering. SIGIR'09, 2009. Google ScholarDigital Library
Index Terms
- Handling data sparsity in collaborative filtering using emotion and semantic based features
Recommendations
Merging trust in collaborative filtering to alleviate data sparsity and cold start
Providing high quality recommendations is important for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Collaborative filtering is a widely accepted technique to generate recommendations based on ...
Collaborative filtering based on an iterative prediction method to alleviate the sparsity problem
iiWAS '09: Proceedings of the 11th International Conference on Information Integration and Web-based Applications & ServicesCollaborative filtering (CF) is one of the most popular recommender system technologies. It tries to identify users that have relevant interests and preferences by calculating similarities among user profiles. The idea behind this method is that, it may ...
Trust-based collaborative filtering: tackling the cold start problem using regular equivalence
RecSys '18: Proceedings of the 12th ACM Conference on Recommender SystemsUser-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers ...
Comments