基于混合分词与串行-并行解码的跨域序列推荐增强
Empowering Cross-Domain Sequential Recommendation with Hybrid Tokenization and Serial-Parallel Decoding
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中文总结 AI 辅助
针对跨域序列推荐中分词忽略跨域协同关联、解码低效的问题，提出GenCDSR框架，通过混合分词与串行-并行解码实现准确率提升与推理延迟降低。
中文摘要 AI 辅助
跨域序列推荐（CDSR）旨在建模用户在多个领域间的动态兴趣转移与序列模式。近年来，生成式推荐（GR）兴起，其先从物品语义中学习语义标识符（SIDs），并将推荐建模为自回归生成。然而，现有方法存在两个关键问题：（1）分词阶段忽略了跨域的协同关联；（2）生成阶段采用低效解码策略（如集束搜索），阻碍了实时部署。为解决这些局限，我们提出GenCDSR，一种针对CDSR的高效生成式框架。具体而言，我们设计了一种带有多塔架构的跨域混合分词机制，通过分层的共享-特定及细粒度码本，联合捕获跨域共性与领域特异性差异。此外，我们开发了一种跨域串行-并行解码策略，利用分层SID结构对生成进行部分并行化，在保持生成一致性的同时显著降低推理延迟。在三个公开数据集上的实验表明，与最先进的基线方法相比，GenCDSR实现了平均1.5%的准确率提升和平均85.1%的推理延迟降低。实现代码与数据集可在线获取：this https URL。
英文摘要
Cross-domain sequential recommendation (CDSR) aims to model users' dynamic interest transitions and sequential patterns across multiple domains. Recently, generative recommendation (GR) has emerged. It first learns semantic identifiers (SIDs) from item semantics and formulates recommendation as autoregressive generation. However, existing methods face two critical issues: (1) they ignore collaborative correlations across domains during tokenization, and (2) they adopt inefficient decoding strategies, such as beam search, during generation, which hinders real-time deployment. To address these limitations, we propose GenCDSR, an effective and efficient generative framework for CDSR. Specifically, we design a cross-domain hybrid tokenization mechanism with 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. Experiments on three public datasets show that GenCDSR achieves an average accuracy improvement of 1.5 percent and an average inference latency reduction of 85.1 percent compared with state-of-the-art baselines. The implementation code and datasets are available online: https://github.com/Applied-Machine-Learning-Lab/RecSys2026_GenCDSR.
发表机构
- City University of Hong Kong（香港城市大学）
- ByteDance Inc.（字节跳动公司）
机构由 AI 辅助整理，请以论文原文为准。
