Detection of preference shift timing using time-series clustering

Abstract

Recommendation methods help online users to purchase products more easily by presenting products that are likely to match their preferences. In these methods, user profiles are constructed according to past activities on the site. When a user accesses an e-commerce site, the user preferences may change during the course of web shopping. We called this a “preference shift” in this paper. However, conventional recommendation methods suppose that user profiles are static, and therefore these methods cannot follow the preference shift. Here, a novel product recommendation method is proposed, which responds to the preference shift. With use of this recommendation method, the users remain at the site longer than before. This paper discusses the detection method for finding the preference shift timing using time-series clustering. In the proposed method, the products preferred by a user are clustered and the preference shift timing is detected as the change in the clustering results.