Selection bias mitigation in recommender system using uninteresting items based on temporal visibility

Abstract

Most collaborative filtering recommendation algorithms rely too much on the user's historical rating data. However, selection bias is common in explicit feedback data, which makes the learning of user preferences face more challenges. We verify the influence of selection bias on topN recommendation, and propose a data filling strategy using uninteresting items based on temporal visibility to alleviate the selection bias in the data. Specifically, our method includes a weighted matrix factorization model to learn users' pre-use preferences for unrated items. According to the experience of items that users have seen but not interacted show negative preferences, we combine user activity, item popularity and temporal rating information to carry out non-uniform weighting to evaluate the confidence of unrated items as a negative example. Then the items with low pre-use preferences are taken as uninteresting items and filled in a low value to restore the user's real rating distribution. Experiments on two real world datasets show that our algorithm can effectively alleviate the selection bias and improve the recommendation accuracy.