Emotion-Enhanced Dual-Agent Recommendation: Understanding and Leveraging Cognitive Conflicts for Better Personalization

Traditional recommendation systems are largely built upon the “rational-agent” assumption, representing user preferences as static numerical vectors while neglecting the pivotal role of emotions in decision-making. However, according to the dual-system theory in cognitive psychology, human decisions are jointly governed by two interacting subsystems: a rational system responsible for deliberate reasoning and an affective system driven by emotion and intuition. Conflicts between these two systems often lead to inconsistencies between users’ preferences and emotional experiences in real-world recommendation scenarios. To address this challenge, we propose an Emotion-Enhanced Dual-Agent Collaborative Framework (EDACF) that explicitly models and leverages cognitive conflicts between users’ emotional experiences and rational preferences. EDACF introduces user and item agents equipped with separate natural language memories for preference, emotion, and conflict representations, enabling cognitive-level reasoning beyond static numerical modeling. The framework features three key innovations: (1) a conflict detection mechanism that identifies users’ cognitive inconsistency states; (2) a dual-memory update strategy that maintains preference stability while capturing emotional dynamics; and (3) an adaptive reasoning mechanism that adjusts decision weights based on detected conflicts. Extensive experiments demonstrate that EDACF outperforms state-of-the-art baselines by 9.9% in NDCG@10 and 13.1% in MRR@10, with improvements exceeding 32% among user groups with high conflict. These results highlight a paradigm shift in recommendation systems from behavior prediction toward cognitive-level understanding of user decision processes.