An Uncertainty-Aware Imputation Framework for Alleviating the Sparsity Problem in Collaborative Filtering

Abstract

Collaborative Filtering (CF) methods for recommender systems commonly suffer from the data sparsity issue. Data imputation has been widely adopted to deal with this issue. However, existing studies have limitations in the sense that both uncertainty and robustness of imputation have not been taken into account, where there is a high risk that the imputed values are likely to be far from the true values. This paper explores a novel imputation framework, named Uncertainty-Aware Multiple Imputation (UA-MI), which can effectively solve the sparsity issue. Given a (sparse) user-item interaction matrix, our key idea is to quantify uncertainty on each missing entry and then the cells with the lowest uncertainty are selectively imputed. Here, we suggest three strategies for measuring uncertainty in missing user-item interactions, each of which is based on sampling, dropout, and ensemble, respectively. They successfully obtain element-wise mean and variance on the missing entries, where the variance helps determine where in the matrix should be imputed and the corresponding mean values are imputed. Experiments show that our UA-MI framework significantly outperformed the existing imputation strategies.