Item recommendation by predicting bipartite network embedding of user preference

Abstract

With the development of e-commerce, various methodologies have studied to improve recommendation performance. Recently, many deep learning based network embedding approaches are applied to the recommendation domain. However, these approaches still have several limitations, such as the problem of data sparseness and the changing in user preference over time, which cannot be considered. In this paper, we propose a novel method for item recommendation based on network embedding. First, we apply a bipartite network embedding to address the data sparsity problem. Bipartite network embedding is a vector representation method that reflects explicit (i.e., observed data) and implicit relations (i.e., unobserved data). Bipartite network embedding methodology can address the data sparsity problem by using implicit relationship information from applying the random walk approach. Second, we predict future bipartite network embedding of user preference by adopting a Kalman filter to consider the changes in user preferences. We have conducted experiments to evaluate the effectiveness and performance of the proposed recommendation method. Through experimentation, the proposed recommendation method is validated as outperforming than the existing approaches including existing network embedding methods.