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Using Adjective Features from User Reviews to Generate Higher Quality and Explainable Recommendations

  • Xiaoying Xu
  • Anindya Datta
  • Kaushik Dutta
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 389)

Abstract

Recommender systems have played a significant role in alleviating the “information overload” problem. Existing Collaborative Filtering approaches face the data sparsity problem and transparency problem, and the content-based approaches suffer the problem of insufficient attributes. In this paper, we show that abundant adjective features embedded in user reviews can be used to characterize movies as well as users’ taste. We extend the standard TF-IDF term weighting scheme by introducing cluster frequency (CLF) to automatically extract high quality adjective features from user reviews for recommendation. We also develop a movie recommendation framework incorporating adjective features to generated highly accurate rating prediction and high quality recommendation explanation. The results of experiments performed on a real world dataset show that our proposed method outperforms the state-of-the-art techniques.

Keywords

Recommender systems User reviews Adjective Features Sparsity Transparency 

References

  1. 1.
    Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, pp. 175–186. ACM (1994)Google Scholar
  2. 2.
    Lops, P., Gemmis, M., Semeraro, G.: Content-based Recommender Systems: State of the Art and Trends. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer (2011)Google Scholar
  3. 3.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Advances in Artificial Intelligence 4 (2009)Google Scholar
  4. 4.
    Adomavicius, G., Tuzhilin, A.: Toward 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, 734–749 (2005)CrossRefGoogle Scholar
  5. 5.
    Sinha, R., Swearingen, K.: The role of transparency in recommender systems. In: CHI 2002: Extended Abstracts on Human Factors in Computing Systems, pp. 830–831. ACM (2002)Google Scholar
  6. 6.
    Cramer, H., Evers, V., Ramlal, S., van Someren, M., Rutledge, L., Stash, N., Aroyo, L., Wielinga, B.: The effects of transparency on trust in and acceptance of a content-based art recommender. User Modeling and User-Adapted Interaction 18, 455–496 (2008)CrossRefGoogle Scholar
  7. 7.
    Massa, P., Avesani, P.: Trust Metrics in Recommender Systems. In: Karat, J., Vanderdonckt, J., Golbeck, J. (eds.) Computing with Social Trust, pp. 259–285. Springer, London (2009)CrossRefGoogle Scholar
  8. 8.
    Wei, C., Hsu, W., Lee, M.: A unified framework for recommendations based on quaternary semantic analysis. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1023–1032. ACM (2011)Google Scholar
  9. 9.
    Jakob, N., Weber, S.-H., Müller, M.-C., Gurevych, I.: Beyond the stars: exploiting free-text user reviews to improve the accuracy of movie recommendations. In: Proceedings of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion, pp. 57–64. ACM, Hong Kong (2009)CrossRefGoogle Scholar
  10. 10.
    Faridani, S.: Using canonical correlation analysis for generalized sentiment analysis, product recommendation and search. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 355–358. ACM (2011)Google Scholar
  11. 11.
    Leung, C.W.K., Chan, S.C.F., Chung, F.: Integrating Collaborative Filtering and Sentiment Analysis: A Rating Inference Approach. In: Proceedings of The ECAI 2006 Workshop on Recommender Systems, pp. 62–66 (2006)Google Scholar
  12. 12.
    Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 173–180. Association for Computational Linguistics, Edmonton (2003)Google Scholar
  13. 13.
    Blei, D., Ng, A., Jordan, M.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  14. 14.
  15. 15.
    Shani, G., Gunawardana, A.: Evaluating Recommendation Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer (2011)Google Scholar
  16. 16.
    Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: World Wide Web, pp. 285–295 (2001)Google Scholar
  17. 17.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. Advances in Neural Information Processing Systems 20, 1257–1264 (2008)Google Scholar
  18. 18.
    Takács, G., Pilászy, I., Németh, B., Tikk, D.: Matrix factorization and neighbor based algorithms for the netflix prize problem. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 267–274. ACM, Lausanne (2008)CrossRefGoogle Scholar
  19. 19.
    de Gemmis, M., Lops, P., Semeraro, G., Basile, P.: Integrating tags in a semantic content-based recommender. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 163–170. ACM (2008)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Xiaoying Xu
    • 1
  • Anindya Datta
    • 1
  • Kaushik Dutta
    • 1
  1. 1.Department of Information Systems, School of ComputingNational University of SingaporeSingapore

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