A Mixture Imputation-Boosted Collaborative Filter
- Xiaoyuan Su
- Taghi Khoshgoftaar, Computer Science and Engineering, Florida Atlantic University, Boca Raton FL 33431, USA
- Russ Greiner, Dept of Computing Science; PI of AICML
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 naive Bayes or mean imputation, depending on the sparsity of the original CF rating dataset. Our empirical results show that IBCFs are fairly accurate on CF tasks, and that IBCF-NBM significantly outperforms a representative hybrid CF system, content-boosted CF algorithm, as well as other IBCFs that use standard imputation techniques.
Citation
X. Su, T. Khoshgoftaar, R. Greiner. "A Mixture Imputation-Boosted Collaborative Filter". Florida AI Research Symposium, pp 312--317, May 2008.| Keywords: | collaborative filtering, imputation techniques, incomplete data, data mining, machine learning |
| Category: | In Conference |
BibTeX
@incollection{Su+al:FLAIRS08,
author = {Xiaoyuan Su and Taghi Khoshgoftaar and Russ Greiner},
title = {A Mixture Imputation-Boosted Collaborative Filter},
Pages = {312--317},
booktitle = {Florida AI Research Symposium},
year = 2008,
}Last Updated: July 29, 2008Submitted by Xiaoyuan Su
