Abstract

Sequential recommendation tracks users' preferences over time based on users' historical activities and makes prediction on their next most probable action. However, this approach faces limitations when dealing with cold-start users who possess minimal interaction data, leading to difficulty in learning their preferences. To address this challenge, by taking regular users with longer interaction histories and cold-start users as two domains, this paper introduces domain adaptation techniques to narrow the performance gap caused by knowledge shifts in domains. We propose a dual-transformer framework with separate models for long (source) and short (target) sequences, collaboratively trained with shared item embeddings. To enable effective knowledge transfer, we introduce an emulated target domain by sampling short sequences from the source, and apply contrastive learning to align their contextual representations. To further improve adaptation under complex knowledge shifts, we reduce item popularity bias and incorporate user similarity into the contrastive loss. Experiments on five public datasets show consistent improvements over strong baselines, demonstrating the robustness of our approach under both length shifts and compounded shifts involving item distribution changes.