Towards purchase prediction: A transaction-based setting and a graph-based method leveraging price information. (May 2021)

• Record Type: Journal Article
• Authors: Li, Zongxi; Xie, Haoran; Xu, Guandong; Li, Qing; Leng, Mingming; Zhou, Chi
• Is Part Of: Pattern recognition. Volume 113(2021)
• Publication Date: 2021-05
• Subjects: Purchase prediction -- Graph-based method -- e-commerce -- Transaction-level data; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.107824
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract : highlights: A new form of transaction-based purchase prediction task is defined. Price and discount features are leveraged explicitly for purchase prediction task. The first study which exploits the graph-based model for purchase prediction task. The graph-based model achieves notable improvement than baseline models. 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.