G-TransRec: A Transformer-Based Next-Item Recommendation With Time Prediction

Abstract

Recently, due to the surge in e-commerce, growing attention has been paid to how to recommend a customer's next purchase based on sequential or session-based data. However, most prior studies have generally focused on what items may be interesting for users, but have neglected the consideration of when the next items are likely to be purchased. Clearly, the timing information is an essential factor for companies to adopt proper selling strategies at the “right” time. In this study, a novel recommendation system, G-TransRec, is proposed to predict customers’ next items of interest with the potential purchase time by exploiting a user temporal interaction sequence. Moreover, by integrating the graph embedding technique, we include the global user information to explore more collaborative knowledge for effective recommendations. Several experiments were conducted on two real datasets to demonstrate the performance and superiority of the proposed model compared with the state-of-the-art methods on several evaluation metrics. We also use a case study to show the practicability of the proposed G-TransRec for users to recommend what they want at what time from a massive amount of merchandise.