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

通过混合标记化与串并行解码赋能跨域序列推荐

Abstract: 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.

摘要： 跨域序列推荐（CDSR）旨在建模用户在多个领域中的动态兴趣转移和序列模式。近年来，生成式推荐（GR）应运而生。它首先从物品语义中学习语义标识符（SIDs），并将推荐任务表述为自回归生成过程。然而，现有方法面临两个关键问题：（1）在标记化过程中忽略了跨域的协同相关性；（2）在生成过程中采用了低效的解码策略（如束搜索），这阻碍了其实时部署。

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.

为了解决这些局限性，我们提出了 GenCDSR，这是一个针对 CDSR 的高效生成式框架。具体而言，我们设计了一种具有多塔架构的跨域混合标记化机制，通过分层的共享-特定码本和细粒度码本，联合捕捉跨域共性与领域特异性。

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.

此外，我们开发了一种跨域串并行解码策略，利用分层 SID 结构实现生成过程的部分并行化，在保持生成一致性的同时显著降低了推理延迟。在三个公开数据集上的实验表明，与当前最先进的基准模型相比，GenCDSR 的平均准确率提升了 1.5%，平均推理延迟降低了 85.1%。相关实现代码和数据集已在线发布。