Advertisement

Exploring Review Content for Recommendation via Latent Factor Model

  • Xiaoyu Chen
  • Yuan Yao
  • Feng Xu
  • Jian Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8862)

Abstract

Recommender systems have been widely studied and applied in many real applications such as e-commerce sites, product review sites, and mobile App stores. In these applications, users can provide their feedback towards the items in the form of ratings, and they usually accompany the feedback with a few words (i.e., review content) to justify their ratings. Such review content may contain rich information about user tastes and item characteristics. However, existing recommendation methods (e.g., collaborative filtering) mainly make use of the historical ratings while ignore the content information. In this paper, we propose to explore the review content for better recommendation via latent factor model. In particular, we propose two strategies to leverage the review content. The first strategy incorporates review content as a guidance term to guide the learnt latent factors of user preferences; the second strategy formulates a regularization term to constrain the preference differences between similar users. Experimental evaluations on two real data sets demonstrate the usefulness of review content and the effectiveness of the proposed method for recommendation.

Keywords

Recommender system latent factor model review content guidance term regularization term 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  2. 2.
    Lü, L., Medo, M., Yeung, C.H., Zhang, Y.C., Zhang, Z.K., Zhou, T.: Recommender systems. Physics Reports 519, 1–49 (2012)CrossRefGoogle Scholar
  3. 3.
    Xue, G.R., Lin, C., Yang, Q., Xi, W., Zeng, H.J., Yu, Y., Chen, Z.: Scalable collaborative filtering using cluster-based smoothing. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005, pp. 114–121. ACM, New York (2005)Google Scholar
  4. 4.
    Khabbaz, M., Lakshmanan, L.V.S.: Toprecs: Top-k algorithms for item-based collaborative filtering. In: Proceedings of the 14th International Conference on Extending Database Technology, EDBT/ICDT 2011, pp. 213–224. ACM, New York (2011)Google Scholar
  5. 5.
    Hofmann, T.: Collaborative filtering via gaussian probabilistic latent semantic analysis. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, SIGIR 2003, pp. 259–266. ACM, New York (2003)CrossRefGoogle Scholar
  6. 6.
    Zhang, Y., Koren, J.: Efficient bayesian hierarchical user modeling for recommendation system. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007, pp. 47–54. ACM, New York (2007)Google Scholar
  7. 7.
    Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, vol. 20. MIT Press, Cambridge (2007)Google Scholar
  8. 8.
    Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using markov chain monte carlo. In: Proceedings of the 25th International Conference on Machine Learning, ICML 2008, pp. 880–887. ACM, New York (2008)Google Scholar
  9. 9.
    Moghaddam, S., Ester, M.: Opinion digger: An unsupervised opinion miner from unstructured product reviews. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 1825–1828. ACM, New York (2010)Google Scholar
  10. 10.
    Moghaddam, S., Ester, M.: Ilda: Interdependent lda model for learning latent aspects and their ratings from online product reviews. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 665–674. ACM, New York (2011)Google Scholar
  11. 11.
    Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: A rating regression approach. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 783–792. ACM, New York (2010)Google Scholar
  12. 12.
    Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis without aspect keyword supervision. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, pp. 618–626. ACM, New York (2011)Google Scholar
  13. 13.
    Jo, Y., Oh, A.H.: Aspect and sentiment unification model for online review analysis. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 815–824. ACM, New York (2011)Google Scholar
  14. 14.
    Brody, S., Elhadad, N.: An unsupervised aspect-sentiment model for online reviews. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, HLT 2010, pp. 804–812. Association for Computational Linguistics, Stroudsburg (2010)Google Scholar
  15. 15.
    Agarwal, D., Chen, B.C.: flda: Matrix factorization through latent dirichlet allocation. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM 2010, pp. 91–100. ACM, New York (2010)Google Scholar
  16. 16.
    McAuley, J., Leskovec, J.: Hidden factors and hidden topics: Understanding rating dimensions with review text. In: Proceedings of the 7th ACM Conference on Recommender Systems, RecSys 2013, pp. 165–172. ACM, New York (2013)CrossRefGoogle Scholar
  17. 17.
    Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 287–296. ACM, New York (2011)Google Scholar
  18. 18.
    Jindal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, WSDM 2008, pp. 219–230. ACM, New York (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiaoyu Chen
    • 1
    • 2
  • Yuan Yao
    • 1
    • 2
  • Feng Xu
    • 1
    • 2
  • Jian Lu
    • 1
    • 2
  1. 1.State Key Laboratory for Novel Software TechnologyNanjingChina
  2. 2.Department of Computer Science and TechnologyNanjing UniversityChina

Personalised recommendations