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Combining Collaborative Filtering and Sentiment Classification for Improved Movie Recommendations

  • Vivek Kumar Singh
  • Mousumi Mukherjee
  • Ghanshyam Kumar Mehta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7080)

Abstract

Recommender systems are traditionally of following three types: content-based, collaborative filtering and hybrid systems. Content-based methods are limited in their applicability to textual items only, whereas collaborative filtering due to its accuracy and its black box approach has been used widely for different kinds of item recommendations. Hybrid method, the third approach, tries to combine content and collaborative approaches to improve the recommendation results. In this paper, we present an alternative approach to a hybrid recommender system that improves the results of collaborative filtering by incorporating a sentiment classifier in the recommendation process. We have explored this idea through our experimental work in movie review domain, with collaborative filtering doing first level filtering and the sentiment classifier performing the second level of filtering. The final recommendation list is a more accurate and focused set.

Keywords

Collaborative Filtering Movie Review Mining Opinion Analysis Recommender Systems Sentiment Classification 

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References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2006)CrossRefGoogle Scholar
  2. 2.
    Alag, S.: Collective Intelligence in Action. Manning, New York (2009)Google Scholar
  3. 3.
    Dave, K., Lawerence, S., Pennock, D.: Mining the Peanut Gallery-Opinion Extraction and Semantic Classification of Product Reviews. In: Proceedings of the 12th International World Wide Web Conference, pp. 519–528 (2003)Google Scholar
  4. 4.
    Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of ACL 2002, 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, US, pp. 417–424 (2002)Google Scholar
  5. 5.
    Esuli, A., Sebastiani, F.: Determining the Semantic Orientation of terms through gloss analysis. In: Proceedings of CIKM 2005, 14th ACM International Conference on Information and Knowledge Management, Bremen, DE, pp. 617–624 (2005)Google Scholar
  6. 6.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classificationusing machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Philadelphia, US, pp. 79–86 (2002)Google Scholar
  7. 7.
    Kim., S.M., Hovy, E.: Determining sentiment of opinions. In: Proceedings of the COLING Conference, Geneva (2004)Google Scholar
  8. 8.
    Durant, K.T., Smith, M.D.: Mining Sentiment Classification from Political Web Logs. In: Proceedings of WEBKDD 2006. ACM (2006)Google Scholar
  9. 9.
    Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  10. 10.
    Turney, P., Littman, M.L.: Unsupervised Learning of Semantic Orientation from a Hundred-Billion-Word corpus. NRC Publications Archive (2002)Google Scholar
  11. 11.
    Esuli, A., Sebastiani, F.: SentiWordNet: A Publicly available lexical resource for opinion mining. In: Proceedings of the Fifth Conference on Language Resources and Evaluation (LREC 2006), Geneva (2006)Google Scholar
  12. 12.
  13. 13.
    Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval, pp. 238–258. Cambridge University Press, New York (2008)CrossRefzbMATHGoogle Scholar
  14. 14.
    Movielens dataset, http://grouplens.org/node/73
  15. 15.
    Internet Movie Database, http://www.imdb.com
  16. 16.
  17. 17.
    Singh, V.K., Mukherjee, M., Mehta, G.K.: Combining a Content Filtering Heuristic and Sentiment Analysis for Movie Recommendations. In: Venugopal, K.R., Patnaik, L.M. (eds.) ICIP 2011. CCIS, vol. 157, pp. 659–664. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vivek Kumar Singh
    • 1
  • Mousumi Mukherjee
    • 2
  • Ghanshyam Kumar Mehta
    • 2
  1. 1.Department of Computer ScienceSouth Asian UniversityNew DelhiIndia
  2. 2.Department of Computer ScienceBanaras Hindu UniversityVaranasiIndia

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