Group-constrained latent Dirichlet allocation for fashion recommendation

Abstract

Recent advances in machine learning have provided valuable tools for constructing various recommendation systems in e-commerce companies such as Amazon and eBay. This paper analyzes click history records from an online fashion mall using a well-known Bayesian topic model, the latent Dirichlet allocation (LDA). Although LDA has popularly been used in the recommendation, a naive algorithm based on the LDA in fashion item recommendation may yield a crucial issue. For a customer who clicked pants primarily, for example, the algorithm tends to recommend pants only. Given a click history of pants, a more desirable algorithm would recommend fashion items compatible with the clicked pants, such as T-shirts, jumpers, and shoes. For this purpose, we propose an algorithm based on a novel Bayesian model, called the group-constrained LDA, which can incorporate prior information about the item groups. Furthermore, with the online stochastic variational inference, the model can accommodate a variety of problems that analyze massive document collections and can be applied to streaming data. The proposed method is applied to the click history data from real online fashion mall, which is the one of the largest online fashion malls in South Korea. In the paper, we demonstrate the benefits of GLDA versus LDA with respect to distinct recommendation problems.