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

• Type: Conference
• Year: 2008
• Where: FLAIRS
• Authors: Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner
• Keywords: Artificial Intelligence, Cf Algorithm, Collaborative Filtering, FLAIRS 2008, Standard Imputation Techniques