UniGCRec: Unified User-Item Quantization for Generative Cross-Domain Recommendation

Abstract

Cross-domain sequential recommendation (CDSR) improves target-domain prediction by leveraging multi-domain interaction histories. Most CDSR methods rely on shared entities or co-occurrence signals, which become unreliable when overlap is limited, and atomic ID representations further generalize poorly to long-tail or unseen items as cross-domain distribution shifts exacerbate this problem. Recent generative CDSR methods enable cross-domain transfer without relying on raw ID alignment by generating content-grounded semantic IDs (SIDs) for cross-domain alignment. However, two challenges remain, including (i) user-item asymmetry, with items discretized for generation whereas user preferences are encoded only implicitly in sequence representations, limiting semantic-level preference control; and (ii) selective transfer, making it difficult to assess source-domain signals against the target preference representation without an explicit discrete user anchor aligned with item IDs, which can lead to unintended transfer of irrelevant signals. This work presents UniGCRec, which constructs a cross-domain user profile from multi-domain histories and quantizes both users and items into CSC-IDs that integrate semantic and collaborative signals. This symmetric quantization places user and item representations in the same discrete CSC-ID space, effectively mitigating user-item asymmetry and enabling preference-aware selective transfer under low-overlap settings. The generator is conditioned on a user CSC-ID prefix and the target domain item CSC-ID history for next-item generation, with trie-constrained decoding ensuring target domain validity. Experiments on public multi-domain benchmarks show consistent gains over strong baselines, with particularly strong gains on several target domains.