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Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (6): 1230-1240.

Special Issue: Data Management and Data Mining

• Data Management and Data Mining •

Modeling Temporal Dynamics of Users' Purchase Behaviors for Next Basket Prediction

Pengfei Wang1, Yongfeng Zhang2, Shuzi Niu3, Jiafeng Guo4, Member, CCF, ACM

1. 1 School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China;
2 Department of Computer Science, Rutgers University, New Jersey 07450, U.S.A.;
3 Institute of Software, Chinese Academy of Sciences, Beijing 100190, China;
4 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
• Received:2018-11-30 Revised:2019-09-10 Online:2019-11-16 Published:2019-11-16
• About author:Pengfei Wang received his B.S. degree in software engineering from the Xidian University, Xi'an, in 2008, his M.S. degree in software engineering from the Beihang University, Beijing, in 2011, and his Ph.D. degree in computer software theory from the Institute of Computing Technology, Chinese Academic of Sciences, Beijing, in 2017. He joined the Beijing University of Posts and Telecommunications, Beijing, as a lecturer in 2017. His research interests include data mining, machine learning, and recommendation. He has published more than 10 papers in refereed journals and conferences.
• Supported by:
This research work was supported by the National Natural Science Foundation of China under Grant Nos. 61802029, and 61602451, and the Fundamental Research for the Central Universities of China under Grant No. 500419741.

Next basket prediction attempts to provide sequential recommendations to users based on a sequence of the user's previous purchases. Ideally, a good prediction model should be able to explore the personalized preference of the users, as well as the sequential relations of the items. This goal of modeling becomes even more challenging when both factors are time-dependent. However, existing methods either take these two aspects as static, or only consider temporal dynamics for one of the two aspects. In this work, we propose the dynamic representation learning approach for time-dependent next basket recommendation, which jointly models the dynamic nature of user preferences and item relations. To do so, we explicitly model the transaction timestamps, as well as the dynamic representations of both users and items, so as to capture the personalized user preference on each individual item dynamically. Experiments on three real-world retail datasets show that our method significantly outperforms several state-of-the-art methods for next basket recommendation.

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