SRSUPM: Sequential Recommender System Based on User Psychological Motivation
This work addresses a specific bottleneck in sequential recommendation for improving user modeling, representing an incremental advancement in the field.
The paper tackles the problem of sequential recommender systems lacking explicit modeling of psychological motivation shifts, which hinders their ability to capture distributional patterns and collaborative knowledge. The proposed SRSUPM framework enhances user modeling with shift-aware components, achieving consistent performance improvements over baselines on three public benchmarks.
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.