Analysis of Customer Characteristics using an Embedding Model with Attention Mechanism Considering Customer and Product Auxiliary Information

Abstract

With the recent popularity of e-commerce websites, analyzing customer characteristics from purchase history data has become an important challenge for companies. One effective approach is to describe the characteristics using embedding representations, and “user2vec” is a classic model using neural networks. However, user2vec does not take into account auxiliary information, and the relation between the customer and product is not clearly considered into model learning process. To more accurately capture the characteristics, it is effective to consider auxiliary information, and to evaluate the relationship of products for each customer. In this study, we propose a model that learns both customer and product-specific embedding representations and auxiliary information embedding representations simultaneously, and uses the attention mechanism to associate customers and products. In addition, we perform an evaluation experiment with artificial data assuming purchase history, to demonstrate the effectiveness of the proposed model. Furthermore, we apply the proposed model to actual movie evaluation data, and show a case of customer characteristic analysis.