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 commonlyused neighborhood based CF, Pearson correlationbased 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.
In REF two imputed neighborhood based collaborative filtering algorithms are proposed that improved the performance of recommender system in very sparse rating data.
Imputed Neighborhood Based Collaborative Filtering
2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
Computer Science