Breaking User-Centric Agency: A Tri-Party Framework for Agent-Based Recommendation

• URL: http://arxiv.org/abs/2603.10673v1
• Date: Wed, 11 Mar 2026 11:40:13 GMT
• Title: Breaking User-Centric Agency: A Tri-Party Framework for Agent-Based Recommendation
• Abstract summary: We propose the first Tri-party LLM-agent Recommendation framework (TriRec)<n>TriRec explicitly coordinates user utility, item exposure, and platform-level fairness.<n>We show consistent gains in accuracy, fairness, and item-level utility.
• Score: 59.362016745945375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large language models (LLMs) have stimulated growing interest in agent-based recommender systems, enabling language-driven interaction and reasoning for more expressive preference modeling. However, most existing agentic approaches remain predominantly user-centric, treating items as passive entities and neglecting the interests of other critical stakeholders. This limitation exacerbates exposure concentration and long-tail under-representation, threatening long-term system sustainability. In this work, we identify this fundamental limitation and propose the first Tri-party LLM-agent Recommendation framework (TriRec) that explicitly coordinates user utility, item exposure, and platform-level fairness. The framework employs a two-stage architecture: Stage~1 empowers item agents with personalized self-promotion to improve matching quality and alleviate cold-start barriers, while Stage~2 uses a platform agent for sequential multi-objective re-ranking, balancing user relevance, item utility, and exposure fairness. Experiments on multiple benchmarks show consistent gains in accuracy, fairness, and item-level utility. Moreover, we find that item self-promotion can simultaneously enhance fairness and effectiveness, challenging the conventional trade-off assumption between relevance and fairness. Our code is available at https://github.com/Marfekey/TriRec.

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