Sciweavers

Share
WEBI
2007
Springer

Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts

9 years 4 months ago
Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts
Collaborative filtering (CF) is one of the most successful approaches for recommendation. In this paper, we propose two hybrid CF algorithms, sequential mixture CF and joint mixture CF, each combining advice from multiple experts for effective recommendation. These proposed hybrid CF models work particularly well in the common situation when data are very sparse. By combining multiple experts to form a mixture CF, our systems are able to cope with sparse data to obtain satisfactory performance. Empirical studies show that our algorithms outperform their peers, such as memory-based, pure model-based, pure content-based CF algorithms, and the contentboosted CF (a representative hybrid CF algorithm), especially when the underlying data are very sparse.
Xiaoyuan Su, Russell Greiner, Taghi M. Khoshgoftaa
Added 09 Jun 2010
Updated 09 Jun 2010
Type Conference
Year 2007
Where WEBI
Authors Xiaoyuan Su, Russell Greiner, Taghi M. Khoshgoftaar, Xingquan Zhu
Comments (0)
books