SRSUPM: Sequential Recommender System Based on User Psychological Motivation

2026-02-09

AI Summary

Existing sequential recommendation methods struggle to explicitly model the dynamic evolution of users’ psychological motivations, limiting their ability to capture evolving patterns and associated collaborative signals effectively. To address this limitation, this work proposes a psychological-motivation-aware sequential recommendation framework that enables fine-grained characterization and utilization of user motivation shifts. The framework integrates psychological motivation state assessment (PMSA), dynamic multi-level state modeling, motivation-driven information decomposition regularization, and a motivation-sensitive collaborative matching mechanism. Extensive experiments demonstrate that the proposed approach significantly outperforms state-of-the-art baselines across three public benchmark datasets, achieving consistent performance gains in various sequential recommendation tasks.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Sequential Recommender System
• Psychological Motivation Shift
• User Modeling
• Collaborative Knowledge
• Distributional Patterns

Innovation

• psychological motivation shift
• sequential recommendation
• user modeling
• information decomposition
• collaborative patterns