Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decoding

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Cross-domain sequential recommendation (CDSR) aims to model users’ dynamic interest transitions and sequential patterns across multiple domains. Recently, generative recommendation (GR) has emerged, which first learns semantic identifiers (SIDs) using semantic information of items and models the recommendation task as autoregressive generation. However, it faces two critical issues: 1) ignoring collaborative correlations across different domains in tokenization step and 2) adopting inefficient decoding strategies like beam search in generation step, which hinders GR’s application in real-time services. To address these limitations, we propose GenCDSR, an effective and efficient generative framework for CDSR. Specifically, we design a cross-domain hybrid tokenization mechanism that employs a multi-tower architecture to jointly capture cross-domain commonalities and domain-specific distinctions through hierarchical shared-specific and fine-grained codebooks. Furthermore, we develop a cross-domain serial-parallel decoding strategy that leverages the hierarchical SID structure to partially parallelize generation, significantly reducing inference latency while preserving generation consistency. Experimental results on three public datasets validate that GenCDSR achieves a 1.5% improvement in accuracy and an 85.1% reduction in inference latency on average compared to SOTA baselines. The implementation code and datasets are available online: https://github.com/Applied-Machine-Learning-Lab/RecSys2026_GenCDSR.