Get access

Content-boosted matrix factorization techniques for recommender systems

Authors

  • Jennifer Nguyen,

    1. Computer Science Department, University College London, Gower Street, London, England WC1E 6BT
    Search for more papers by this author
  • Mu Zhu

    Corresponding author
    1. Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada
    • Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada
    Search for more papers by this author

Abstract

Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. These content-boosted matrix factorization algorithms not only improve recommendation accuracy, but also provide useful insights about the contents, as well as make recommendations more easily interpretable. © 2013 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2013

Get access to the full text of this article

Ancillary