Learning Within-Session Budgets from Browsing Trajectories

Authors: Diane Hu, Raphael Louca, Liangjie Hong, Julian McAuley (UC San Diego)

Presented at: Recsys 2018 (Vancouver, BC)

Abstract: Building price- and budget-aware recommender systems is critical in settings where one wishes to produce recommendations that balance users’ preferences (what they like) with a model of purchase likelihood (what they will buy). A trivial solution consists of learning global budget terms for each user based on their past expenditure. To more accurately model user budgets, we also consider a user’s within-session budget, which may deviate from their global budget depending on their shopping context. In this paper, we find that users implicitly reveal their session-specific budgets through the sequence of items they browse within that session. Specifically, we find that some users “browse down,” by purchasing the cheapest item among alternatives under consideration, others “browse up” (selecting the most expensive), and others ultimately purchase items around the middle. Surprisingly, this mixture of behaviors is difficult to observe globally, as individual users tend to belong firmly to one of the three segments. To model this behavior, we develop an interpretable budget model that combines a clustering component to detect different user segments, with a model of segment specific purchase profiles. We apply our model on a dataset of browsing and purchasing sessions from Etsy, a large e- commerce website focused on handmade and vintage goods, where it outperforms strong baselines and existing production systems.