Modeling Missing Data Based on Neural Fuzzy Inference for Implicit Recommendation

Abstract

As implicit feedback can be tracked automatically and is easy to collect, the implicit recommendation attracts more researcher attentions. However, the uncertainty of the implicit feedback meaning poses a great challenge to the implicit recommendation. Especially for the missing data, we are not sure whether the users dislike or just have not seen the items. It may lead to bias of predictions. In this paper, we propose Neural Fuzzy Inference based on User preference and Item popularity (UI-NFI) algorithm to model the missing data in implicit recommendation. First, we use fuzzy set theory to represent user preference and item popularity that get from the history interactions and side information. Furthermore, neural fuzzy inference is proposed to predict the exposure possibility of missing data. Based on the fuzzy inference model, UI-NFI and matrix factorization model perform joint learning to predict. Experimental results show that our model has better performance compared to the other implicit recommendation algorithms.