imputation-boosted collaborative filtering using machine learning classifiers
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 nave 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.
collaborative filtering machine learning classifiers imputation techniques incomplete data recommendation systems