Sequential Recommendation (SR) aims to predict the next interaction of a user based on their behavior sequence, where complementary relations often provide essential signals for predicting the next item. However, mainstream models relying on sparse co-purchase statistics often mistake spurious correlations (e.g., due to popularity bias) for true complementary relations. Identifying true complementary relations requires capturing the fine-grained item semantics (e.g., specifications) that simple cooccurrence statistics would be unable to model. While recent semantics-based methods utilize discrete semantic codes to represent items, they typically aggregate semantic codes into coarse item representations. This aggregation process blurs specific semantic details required to identify complementarity. To address these critical limitations and effectively leverage semantics for capturing reliable complementary relations, we propose a Complementary-Aware Semantic Transition (CAST) framework that introduces a new modeling paradigm built upon semantic-level transitions. Specifically, a semantic-level transition module is designed to model dynamic transitions directly in the discrete semantic code space, effectively capturing fine-grained semantic dependencies often lost in aggregated item representations. Then, a complementary prior injection module is designed to incorporate LLM-verified complementary priors into the attention mechanism, thereby prioritizing complementary patterns over co-occurrence statistics. Experiments on multiple e-commerce datasets demonstrate that CAST consistently outperforms the state-of-the-art approaches, achieving up to 17.6% Recall and 16.0% NDCG gains with 65x training acceleration. This validates its effectiveness and efficiency in uncovering latent item complementarity beyond statistics. The code will be released upon acceptance.

翻译：序列推荐旨在基于用户行为序列预测其下一次交互，其中互补关系通常为预测下一物品提供关键信号。然而，依赖稀疏共现统计数据的主流模型常将虚假关联（如因流行度偏差导致）误判为真实互补关系。识别真实互补关系需要捕获细粒度物品语义（如规格），而简单的共现统计无法建模这些语义。尽管近期基于语义的方法利用离散语义编码表示物品，但其通常将语义编码聚合成粗粒度的物品表示，这一聚合过程模糊了识别互补性所需的具体语义细节。为应对这些关键局限并有效利用语义捕获可靠的互补关系，我们提出互补感知语义转换框架，该框架引入了一种基于语义级转换的新型建模范式。具体而言，我们设计了语义级转换模块，直接在离散语义编码空间中建模动态转换，有效捕获聚合物品表示中常丢失的细粒度语义依赖。然后，设计互补先验注入模块，将经大语言模型验证的互补先验融入注意力机制，从而优先关注互补模式而非共现统计。在多个电子商务数据集上的实验表明，CAST持续优于当前最优方法，在实现65倍训练加速的同时，召回率提升高达17.6%，归一化折损累计增益提升高达16.0%。这验证了其在超越统计层面揭示物品潜在互补性的有效性与高效性。代码将在论文接收后发布。