Unbiased Interest Modeling in Sequential Basket Analysis: Addressing Repetition Bias with Multi-Factor Estimation

Sequential basket analysis is a challenging task that focuses on modeling user interests through their shopping basket records. This study focuses on a newly identified bias: the repetition bias , which typically arises due to repurchase behavior . Existing methods typically oversimplify the relationship between repetitions and predictions. They assume that frequent repetition of an item by a user indicates a strong preference of the user. However, this assumption is flawed as it fails to consider that repetitions are not driven solely by user interests, as they can also be influenced by external factors, resulting in a biased understanding of user interests. In this article, we propose the CA usal intervention for R epetition D e-biasing ( CARD ), a novel solution to comprehensively estimate various influencing factors and address the repetition bias, thereby ensuring a more accurate learning of user interests. Specifically, we design a multi-factor estimation debiasing framework with constructed causal graphs to formalize the data generation process within the recommendation. We then analyze the variables that influence the recommendation, with the goal of identifying confounding variables that affect repurchase behavior and thereby locating the source of repetition bias. Since repetition bias originates from the influence of confounding variables on repurchase behavior, we resort to causal intervention methods to prevent its impacts and thus eliminate repetition bias at its source for unbiased user interest modeling. We evaluate CARD by conducting extensive experiments over three real-world datasets. The results demonstrate our approach’s competitiveness over the representative state-of-the-art baselines in achieving unbiased user interest modeling.