Advertisement

Feature-Weighted User Model for Recommender Systems

  • Panagiotis Symeonidis
  • Alexandros Nanopoulos
  • Yannis Manolopoulos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)

Abstract

Recommender systems are gaining widespread acceptance in e-commerce applications to confront the “information overload” problem. Collaborative Filtering (CF) is a successful recommendation technique, which is based on past ratings of users with similar preferences. In contrast, Content-Based filtering (CB) assumes that each user operates independently. As a result, it exploits only information derived from document or item features. Both approaches have been extensively combined to improve the recommendation procedure. Most of these systems are hybrid: they run CF on the results of CB and vice versa. CF exploits information from the users and their ratings. CB exploits information from items and their features. In this paper, we construct a feature-weighted user profile to disclose the duality between users and features. Exploiting the correlation between users and features we reveal the real reasons of their rating behavior. We perform experimental comparison of the proposed method against the well-known CF, CB and a hybrid algorithm with a real data set. Our results show significant improvements, in terms of effectiveness.

Keywords

User Rating Test User Collaborative Filter Item Feature Recommendation List 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baeza-Yates, R.A., Ribeiro-Neto, B.A.: Modern Information Retrieval. ACM Press / Addison-Wesley (1999)Google Scholar
  2. 2.
    Balabanovic, M., Y, S.: Fab: Content-based, collaborative recommendation. ACM Communications 40(3), 66–72 (1997)CrossRefGoogle Scholar
  3. 3.
    Jin, X., Zhou, Y., Mobasher, B.: A maximum entropy web recommendation system: Combining collaborative and content features. In: Proc. ACM SIGKDD Conf., pp. 612–617 (2005)Google Scholar
  4. 4.
    Melville, P., Mooney, R.J., Nagarajan, R.: Proc. AAAI conf. In Content-Boosted Collaborative Filtering for improved Recommendations, pp. 187–192 (2002)Google Scholar
  5. 5.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering on netnews. In: Proc. Conf. Computer Supported Collaborative Work, pp. 175–186 (1994)Google Scholar
  6. 6.
    Salter, J., Antonopoulos, N.: Cinemascreen recommender agent: Combining collaborative and content-based filtering. Intelligent Systems Magazine 21(1), 35–41 (2006)Google Scholar
  7. 7.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proc. WWW Conf., pp. 285–295 (2001)Google Scholar
  8. 8.
    Yan, W.T., Molina, H.G.: Sift: A tool for wide-area information dissemination. In: Proc. UNSENIX Conf., pp. 177–186 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Panagiotis Symeonidis
    • 1
  • Alexandros Nanopoulos
    • 1
  • Yannis Manolopoulos
    • 1
  1. 1.Aristotle University, Department of Informatics, Thessaloniki 54124Greece

Personalised recommendations