双兴趣序列推荐中的多粒度状态空间模型

中文摘要 AI 辅助

序列推荐旨在根据用户的历史行为预测用户接下来会交互的下一项。Transformer的进展显著提升了序列推荐的效果，但仍受限于成本效率问题。尽管状态空间模型（SSMs）近期已实现高效的长序列建模，但现有方法大多将每个物品编码为单一的静态角色，忽视了物品的多义性现象。实际上，同一物品往往根据用户上下文扮演不同的语义角色，且现有方法在捕捉不同时间粒度上的动态行为方面存在局限。在本工作中，我们提出DSRec，一种双兴趣跨状态空间模型，显式地将物品角色在长期与短期语义上下文中进行解耦。序列物品被编码为长期兴趣嵌入，通过历史聚合捕捉稳定偏好；同时设置短期兴趣分支，强调由交互间隔时间调制的局部会话意图。这些兴趣嵌入通过不同的SSM编码器处理：用于长期建模的全序列Mamba，以及基于时间调制的SSM，其根据时间间隔动态调整状态演化。为实现跨粒度对齐，我们采用残差交叉融合机制，在保持语义独立性的同时，在两个分支间交换上下文信息。在公开基准上的实验表明，DSRec优于其他最先进的方法。

英文摘要

Sequential recommendation aims to predict the next item a user will interact with based on their historical behavior. Advances in Transformers have significantly improved sequential recommendation but are still limited by cost efficiency. Although State Space Models (SSMs) have recently enabled efficient long-range modeling, most existing methods encode each item with a single static contextual role, overlooking the phenomenon of item polysemy. In fact, the same item often plays different semantic roles depending on user context, and existing methods are limited in capturing dynamic behavior across different temporal granularities. In this work, we propose DSRec, a novel dual-interest cross-SSM model that explicitly disentangles item roles across long-term and short-term semantic context. Sequential items are encoded into long-term interest embeddings that capture stable preferences via historical aggregation, and a short-term interest branch that emphasizes local session intent modulated by inter-click time intervals. These interest embeddings are processed through distinct SSM encoders: a full-sequence Mamba for long-term modeling, and a time-modulated SSM that dynamically adjusts state evolution based on temporal gaps. To enable effective cross-granularity alignment, we adopt a residual cross-fusion mechanism that exchanges contextual information between the two branches while preserving semantic independence. Experiments on public benchmarks demonstrate that DSRec outperforms other state-of-the-art methods.

发表机构

• The Hong Kong University of Science and Technology（香港科技大学）

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