SRSUPM: Sequential Recommender System Based on User Psychological Motivation

TL;DR

This paper introduces SRSUPM, a novel sequential recommender system that explicitly models user psychological motivation shifts to improve recommendation accuracy.

Contribution

It proposes a comprehensive framework incorporating psychological motivation shift assessment, dynamic shift modeling, and motivation-aware information decomposition.

Findings

SRSUPM outperforms baseline models on three public benchmarks.

The framework effectively captures psychological motivation shifts.

Experimental results demonstrate improved recommendation performance.

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.