Imputed Neighborhood Based Collaborative Filtering
- 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
Collaborative filtering (CF) is one of the most effective types of recommender systems. As data sparsity remains a significant challenge for CF, we consider basing predictions on imputed data, and find this often improves performance on very sparse rating data. In this paper, we propose two imputed neighborhood based collaborative filtering (INCF) algorithms: imputed nearest neighborhood CF (INN-CF) and imputed densest neighborhood CF (IDN-CF), each of which first imputes the user rating data using an imputation technique, before using a traditional Pearson correlation-based CF algorithm on the resulting imputed data of the most similar neighbors or the densest neighbors to make CF predictions for a specific user. We compared an extension of Bayesian multiple imputation (eBMI) and the mean imputation (MEI) in these INCF algorithms, with the commonly-used neighborhood based CF, Pearson correlation-based CF, as well as a densest neighborhood based CF. Our empirical results show that IDN-CF using eBMI significantly outperforms its rivals and takes less time to make its best predictions.
Citation
X. Su, T. Khoshgoftaar, R. Greiner. "Imputed Neighborhood Based Collaborative Filtering". IEEE/WIC/ACM International Conference on Web Intelligence, Sydney, Australia, pp 633-639, December 2008.| Keywords: | Collaborative Filtering, Recommender Systems, Imputation Techniques, Multiple Imputation |
| Category: | In Conference |
| Web Links: | WI'08 |
BibTeX
@incollection{Su+al:WI08,
author = {Xiaoyuan Su and Taghi Khoshgoftaar and Russ Greiner},
title = {Imputed Neighborhood Based Collaborative Filtering},
Pages = {633-639},
booktitle = {IEEE/WIC/ACM International Conference on Web Intelligence},
year = 2008,
}Last Updated: February 08, 2009Submitted by Xiaoyuan Su
