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Mining Latent Attributes in Neighborhood for Recommender Systems

  • Na ChangEmail author
  • Takao Terano
Chapter
  • 714 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 343)

Abstract

Neighborhood-based collaborative filtering (CF) algorithms have been extensively studied and discussed. In the traditional way of these methods, user-based CF predicts a target user’s preference for an item based on the integrated preference of the user’s neighbors for the item, and item-based CF is based on the integrated preference of the user’s preference for the item’s neighbors. Both the two ways underestimate the effect of structure of the target user or item’s neighbors. That is, for instance, these neighbors may form two distinct groups: some neighbors like the target item or give high ratings; on the other hand, some neighbors dislike the target item or give low ratings. The difference between the two groups may influence user’s choice. As an extension of neighborhood-based collaborative filtering, this paper focuses on the analysis of such structure by mining latent attributes of users or items’ neighborhood, and corresponding correlations with users’ preference by several popular data mining techniques. Mining latent attributes and experiment evaluation were conducted on MovieLens dataset. The experimental results reveal that the proposed method can improve the performance of pure user-based and item-based collaborative filtering algorithm.

Keywords

Recommender systems Collaborative filtering Latent attributes Mining techniques 

References

  1. 1.
    Melville, P., Vikas, S.: Recommender systems. J. Encycl. Mach. Learn. 829–838 (2010)Google Scholar
  2. 2.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2000)CrossRefGoogle Scholar
  3. 3.
    Xu, J., Johnson-Wahrmann K., Li, S.: The development, status and trends of recommender systems: a comprehensive and critical literature review. In: Proceedings of International Conference Mathematics and Computers in Science and Industry. 117–122 (2014)Google Scholar
  4. 4.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. J. Adv. Artif. Intell. (2009)Google Scholar
  5. 5.
    Kularbphettong, K., Somngam, S., Tongsiri, C., Roonrakwit, P.: A Recommender System using Collaborative Filtering and K-mean based on Android Application. In: Proceedings of International Conference Applied Mathematics, Computational Science and Engineering. 161–166 (2014)Google Scholar
  6. 6.
    Xue, G., Lin, C., Yang,Q., Xi, W., Zeng, H., Yu, Y.: Scalable collaborative filtering using cluster-based smoothing. SIGIR’05 August. 15–19 (2005)Google Scholar
  7. 7.
    Wang, W., Chen, Z., Liu, J., Qi Q., Zhao, Z.: User-based Collaborative filtering on cross domain by Tag transfer learning. In: ACM KDD’18 August 12–16 (2012)Google Scholar
  8. 8.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD Conference. 426–434 (2008)Google Scholar
  9. 9.
    Morid, M.A., Shajari, M., Golpayegni, A.H.: Who are the most influential users in a recommender system? In: Proceedings of the 13th International Conference on Electronic Commerce, ACM, New York (2012)Google Scholar
  10. 10.
    Hall, M.A.: Correlation-based feature selection for machine learning. Doctoral dissertation, The University of Waikato (1999)Google Scholar
  11. 11.
    Quinlan, J.R.: Induction of decision trees. J. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  12. 12.
    Rokach, L.: Data mining with decision trees: theory and applications. J. World Scientific. 69 (2008)Google Scholar
  13. 13.
    Miyahara, K., Pazzani, M.J.: Collaborative filtering with the simple bayesian classifier. In: Pacific Rim International Conference on Artificial Intelligence (2000)Google Scholar
  14. 14.
    Zurada, J.: Introduction to artificial neural systems. West Publishing, St. Paul (1992)Google Scholar
  15. 15.
    Berka, T. Behrendt, W., Gams, E.: A trail based internet-domain recommender system using artificial neural networks. In: Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web Based Systems (2002)Google Scholar
  16. 16.
    Kongsakun, K., Fung, C.C.: Neural Network Modeling for an Intelligent Recommendation System Supporting SRM for Universities in Thailand. WSEAS Trans. Comput. 11(2), 34–44 (2012)Google Scholar
  17. 17.
    Cristianini, N., Shawe-Taylor, J.: An introduction to support vector machines and other Kernel-based learning methods. Cambridge University Press, New York (2000)CrossRefGoogle Scholar
  18. 18.
    Kang, H. Yoo, S.: SVM and collaborative filtering-based prediction of user preference for digital fashion recommendation systems. IEICE Trans. Inf. Syst. (2007)Google Scholar
  19. 19.
    Shani, G., Gunawardana, A.: Evaluating recommendation systems. Recommender systems handbook, pp. 257–297. Springer, New York (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computational Intelligence and Systems ScienceTokyo Institute of TechnologyMidori-kuJapan

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