Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Imputed Neighborhood Based Collaborative Filtering
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
An adaptive approach to dealing with unstable behaviour of users in collaborative filtering systems
Journal of Information Science
The efficient imputation method for neighborhood-based collaborative filtering
Proceedings of the 21st ACM international conference on Information and knowledge management
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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.