Imputation-Boosted Collaborative Filtering Using Machine Learning Classifiers
- Xiaoyuan Su
- Taghi Khoshgoftaar, Computer Science and Engineering, Florida Atlantic University, Boca Raton FL 33431, USA
- Xingquan Zhu, Computer Science and Engineering, Florida Atlantic University, Boca Raton FL 33431, USA
- Russ Greiner, Dept of Computing Science; PI of AICML
As data sparsity remains a significant challenge for collaborative filtering (CF), we conjecture that predicted ratings based on imputed data may be more accurate than those based on the originally very sparse rating data. In this paper, we propose a framework of imputation-boosted collaborative filtering (IBCF), which first uses an imputation technique, or perhaps machine learned classifier, to fill-in the sparse user-item rating matrix, then runs a traditional Pearson correlation-based CF algorithm on this matrix to predict a novel rating. Empirical results show that IBCF using machine learning classifiers can improve predictive accuracy of CF tasks. In particular, IBCF using a classifier capable of dealing well with missing data, such as naïve Bayes, can outperform the content-boosted CF (a representative hybrid CF algorithm) and IBCF using PMM (predictive mean matching, a state-of-the-art imputation technique), without using external content information.
Citation
X. Su, T. Khoshgoftaar, X. Zhu, R. Greiner. "Imputation-Boosted Collaborative Filtering Using Machine Learning Classifiers". ACM Symposium on Applied Computing, pp 949-950, March 2008.| Keywords: | Collaborative filtering, recommendation systems, imputation techniques, machine learning classifiers, incomplete data |
| Category: | In Conference |
BibTeX
@incollection{Su+al:SAC08,
author = {Xiaoyuan Su and Taghi Khoshgoftaar and Xingquan Zhu and Russ
Greiner},
title = {Imputation-Boosted Collaborative Filtering Using Machine Learning
Classifiers},
Pages = {949-950},
booktitle = {ACM Symposium on Applied Computing},
year = 2008,
}Last Updated: October 25, 2007Submitted by Xiaoyuan Su
