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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

  • Haoran Xie
  • Guandong Xu
  • Qing Li
  • Mingming Leng
  • Chi Zhou

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number107824
Journal / PublicationPattern Recognition
Volume113
Online published22 Jan 2021
Publication statusPublished - May 2021

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.

Research Area(s)

  • e-commerce, Graph-based method, Purchase prediction, Transaction-level data