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: Contribution to journalArticlespeer-review

14 Citations (Scopus)

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 transaction 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. Copyright © 2021 Elsevier Ltd. All rights reserved.

Original languageEnglish
Article number107824
JournalPattern Recognition
Volume113
Early online dateJan 2021
DOIs
Publication statusPublished - May 2021

Citation

Li, Z., Xie, H., Xu, G., Li, Q., Leng, M., & Zhou, C. (2021). Towards purchase prediction: A transaction-based setting and a graph-based method leveraging price information. Pattern Recognition, 113, Article 107824. https://doi.org/10.1016/j.patcog.2021.107824

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