Modeling user demand evolution for next-basket prediction

Shoujin Wang, Yan Wang*, Liang Hu, Xiuzhen Zhang, Qi Zhang, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Defu Lian

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Users' purchase behaviors are complex and dynamic, which are usually driven by various personal demands evolving with time. According to psychology and economic theories, user demands can be satisfied with a sequence of purchase behaviors, resulting in a basket of items. However, most of the existing works simply predict the next basket from a shallow perspective of (purchase) sequence data modeling without deep insight into the underlying factors which drive user purchase behaviors. In fact, filling a basket with multiple items is a process to incrementally satisfy a user's demand. Therefore, the key challenges to predict a user's next basket lie in (1) how to track the changes of the user's demand, and (2) how to satisfy her demand at a given moment. To this end, we propose an Evolving DEmand SAtisfaction (EvoDESA) model to model a user's demand evolution for next-basket prediction. In EvoDESA, a demand evolution module learns the dynamics of user demand over a sequence of basket-purchase behaviors. Then, a next-basket planning module effectively packs an optimal combination of items to best satisfy the user's current demand. Extensive experiments on three real-world transaction datasets demonstrate the considerable superiority of EvoDESA over the state-of-the-art approaches.
Original languageEnglish
Pages (from-to)11585-11598
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number11
Early online date21 Dec 2022
Publication statusPublished - Nov 2023


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