Towards purchase prediction: A transaction-based setting and a graph-based method leveraging price information. (May 2021) - Li, Zongxi; Xie, Haoran; Xu, Guandong; Li, Qing; Leng, Mingming; Zhou, Chi
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.