Towards purchase prediction: A transaction-based setting and a graph-based method leveraging price information

Zongxi Li, Haoran Xie, Guandong Xu, Qing Li, Mingming Leng, Chi Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Targeting at boosting business revenue, purchase prediction based on user behavior is crucial to e- commerce. However, it is not a well-explored topic due to a lack of relevant datasets. Specifically, no public dataset provides both price and discount information varying on time, which play an essential role in the user’s decision making. Besides, existing learn-to-rank methods cannot explicitly predict the purchase possibility for a specific user-item pair. In this paper, we propose a two-step graph-based model, where the graph model is applied in the first step to learn representations of both users and items over click-through data, and the second step is a classifier incorporating the price information of each transac- tion record. To evaluate the model performance, we propose a transaction-based framework focusing on the purchased items and their context clicks, which contain items that a user is interested in but fails to choose after comparison. Our experiments show that exploiting the price and discount information can significantly enhance prediction accuracy.
Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalPattern Recognition
Volume113
Publication statusPublished - May 2021

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