Persona Identification in E-Commerce with Scarce Labels and In-Context Graph Learning

User personas play a crucial role in shaping shopping behavior, making persona identification an essential task for e-commerce platforms to personalize user experiences. However, this problem remains highly challenging due to several key factors: (1) users often exhibit multiple personas simultaneously (e.g., a fashion enthusiast who is also a sports lover)(2) labeled data is scarce, as annotation requires extensive human supervision(3) real-world user-product interaction data is inherently noisy, as accounts may be shared among multiple individuals, complicating persona assessment, and (4) user-product interactions form a dynamic, heterogeneous bipartite graph where product features are diverse, and personas evolve due to shifts in interests, seasons, and external events. In this work, we study the problem of persona identification on an extensive real-world user-product interaction dataset spanning six months (August 2023 to January 2024), capturing user behavior influenced by real-world factors such as seasonal changes, festivals (e.g., Christmas, New Year), and major sales events. To model persona identification in this evolving interaction graph, we reformulate the multi-label node classification task as a link prediction problem, enabling a structured decoupling of user and persona representations. To this end, we propose TRIPER, a novel TRIpartite graph neural network specifically designed to enhance multi-label PERsona classification along with in-context inference capabilities. Extensive evaluations on our real-world dataset demonstrate that TRIPER achieves high predictive accuracy in user persona identification, exhibits strong generalization over time, and effectively learns from limited labeled data, outperforming state-of-the-art baselines.