Imad Eddine Khiloun

Analyzing and Mitigating Asymmetric Learning for Product Cold-Start in E-Commerce Purchase Prediction with Graph Neural Networks: Similarity-Driven History Augmentation

This paper proposes Similarity-Driven History Augmentation (SHA), a data-centric approach that assigns synthetic interaction histories to cold-start products by matching them with semantically similar established donors and introduces a decoupled hybrid framework alongside an enhanced SHA strategy that selectively filt...