Content-boosted Matrix Factorization Techniques for Recommender Systems

  title={Content-boosted Matrix Factorization Techniques for Recommender Systems},
  author={Jennifer Nguyen and Mu Zhu},
  journal={Statistical Analysis and Data Mining},
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… CONTINUE READING
Highly Cited
This paper has 20 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 6 times over the past 90 days. VIEW TWEETS
12 Citations
42 References
Similar Papers


Publications citing this paper.

Similar Papers

Loading similar papers…