Sciweavers

Share
FLAIRS
2008

A Mixture Imputation-Boosted Collaborative Filter

8 years 10 months ago
A Mixture Imputation-Boosted Collaborative Filter
Recommendation systems suggest products to users. Collaborative filtering (CF) systems, which base those recommendations on a database of previous ratings by various users and products, have been proven to be very effective. Since this database is typically very sparse, we consider first imputing the missing values, then making predictions based on that completed dataset. In this paper, we apply several standard imputation techniques within the framework of imputation-boosted collaborative filtering (IBCF). Each technique passes that imputed rating data to a traditional Pearson correlation-based CF algorithm, which uses that information to produce CF predictions. We also propose a novel mixture IBCF algorithm, IBCF-NBM, that uses either na
Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greine
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2008
Where FLAIRS
Authors Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner
Comments (0)
books