Cross-domain sequential recommendation (CDSR) alleviates interaction sparsity by jointly modeling user behaviors across multiple domains. While current studies have made some progresses, they still neglect two issues that severely impact recommendation performance: (i) ignoring domain-specific interaction frequencies and interest decay rates at identical time intervals; (ii) treating semantic preferences as time-invariant during cross-domain transfer. To address these, we propose a novel framework that bridges Behavior and Semantics for Time-aware Cross-Domain Sequential Recommendation (BST-CDSR). Specifically, we design a behavioral preference evolution module that decouples long-term interests and short-term intentions, and models continuous-time preference via a neural ordinary differential equation (ODE) with event-driven updates. Additionally, to capture time-aware semantic preferences, we introduce a temporal counterfactual-enhanced semantic generator that discretizes temporal interval tokens and leverages large language models (LLMs) to extract robust temporal semantics, where counterfactual perturbations enhance the time sensitivity of semantic preferences. Furthermore, we propose a time-preference guided domain transfer module to adaptively control transfer weights and mitigate negative transfer. Extensive experiments on real-world datasets demonstrate that BST-CDSR consistently outperforms baselines.

翻译：跨域序列推荐通过联合建模用户在多个域中的行为来缓解交互稀疏性问题。现有研究虽取得一定进展，但仍忽视了严重影响推荐性能的两个问题：（i）忽略相同时间间隔下域特定交互频率与兴趣衰减速率的差异；（ii）在跨域迁移过程中将语义偏好视为与时间无关。为解决这些问题，我们提出了一种新型框架——面向时间的跨域序列推荐中的行为与语义桥接。具体而言，我们设计了一个行为偏好演化模块，该模块分离长期兴趣与短期意图，并通过事件驱动更新的神经常微分方程建模连续时间偏好。此外，为捕捉面向时间的语义偏好，我们引入了一个时序反事实增强语义生成器，该生成器离散化时间间隔令牌并利用大语言模型提取鲁棒的时间语义特征，其中反事实扰动增强了语义偏好的时间敏感性。进一步，我们提出了一种时间偏好引导的域迁移模块，以自适应控制迁移权重并缓解负迁移。在真实数据集上的大量实验表明，BST-CDSR始终优于基线方法。