Sequential recommender infers users' evolving psychological motivations from historical interactions to recommend the next preferred items. Most existing methods compress recent behaviors into a single vector and optimize it toward a single observed target item, but lack explicit modeling of psychological motivation shift. As a result, they struggle to uncover the distributional patterns across different shift degrees and to capture collaborative knowledge that is sensitive to psychological motivation shift. We propose a general framework, the Sequential Recommender System Based on User Psychological Motivation, to enhance sequential recommenders with psychological motivation shift-aware user modeling. Specifically, the Psychological Motivation Shift Assessment quantitatively measures psychological motivation shift; guided by PMSA, the Shift Information Construction models dynamically evolving multi-level shift states, and the Psychological Motivation Shift-driven Information Decomposition decomposes and regularizes representations across shift levels. Moreover, the Psychological Motivation Shift Information Matching strengthens collaborative patterns related to psychological motivation shift to learn more discriminative user representations. Extensive experiments on three public benchmarks show that SRSUPM consistently outperforms representative baselines on diverse sequential recommender tasks.

翻译：序列推荐通过历史交互推断用户不断演化的心理动机，从而预测下一个偏好项目。现有方法大多将近期行为压缩为单一向量并仅针对单个观测目标项进行优化，缺乏对心理动机转移的显式建模。因此，这些方法难以揭示不同转移程度下的分布模式，也难以捕获对心理动机转移敏感的协同知识。我们提出通用框架——基于用户心理动机的序列推荐系统（SRSUPM），通过心理动机转移感知的用户建模增强序列推荐性能。具体而言，心理动机转移评估模块（PMSA）定量度量心理动机转移程度；在PMSA指导下，转移状态构建模块动态建模多层级转移状态，而心理动机转移驱动的信息分解模块对跨转移层级的表征进行分解与正则化。此外，心理动机转移信息匹配模块增强与心理动机转移相关的协同模式，以学习更具判别性的用户表征。在三个公开基准上的大量实验表明，SRSUPM在各种序列推荐任务中始终优于代表性基线方法。