Shopping Trajectory Representation Learning with Pre-training for E-commerce Customer Understanding and Recommendation

Abstract

Understanding customer behavior is crucial for improving service quality in large-scale E-commerce. This paper proposes C-STAR, a new framework that learns compact representations from customer shopping journeys, with good versatility to fuel multiple downstream customer-centric tasks. We define the notion of shopping trajectory that encompasses customer interactions at the level of product categories, capturing the overall flow of their browsing and purchase activities. C-STAR excels at modeling both inter-trajectory distribution similarity-the structural similarities between different trajectories, and intra-trajectory semantic correlation-the semantic relationships within individual ones. This coarse-to-fine approach ensures informative trajectory embeddings for representing customers. To enhance embedding quality, we introduce a pre-training strategy that captures two intrinsic properties within the pre-training data. Extensive evaluation on large-scale industrial and public datasets demonstrates the effectiveness of C-STAR across three diverse customer-centric tasks. These tasks empower customer profiling and recommendation services for enhancing personalized shopping experiences on our E-commerce platform.