An encoder-decoder switch network for purchase prediction

Abstract

Users in e-commerce tend to click on items of their interest. Eventually, the more frequently an item is clicked by a user, the more likely the item will be purchased by the user after all. However, what if a user clicked on every item only once before purchases? This is a frequently observed user behavior in reality, but predicting which of the clicked items will be purchased is a challenging task. This paper addresses a practical yet widely overlooked task of predicting purchase items within a non-duplicate click session, i.e., a session in which every item is clicked only once. We propose an encoder-decoder neural architecture to simultaneously model users' click and purchase behaviors. The encoder captures a user's intent contained in the user's click session, and the decoder, which is equipped with pointer network via a switch gate, extracts relevant clicked items for future purchase candidates. To the best of our knowledge, our work is the first to address the task of purchase prediction given non-duplicate click sessions. Experiments demonstrate that our proposed method outperforms the state-of-the-art purchase prediction methods by up to 18% in terms of recall. (C) 2019 Elsevier B.V. All rights reserved.