Abstract:Large language models (LLMs) have spurred interest in agent-based recommender systems, yet most agentic approaches remain user-centric: items stay passive entities whose exposure is a by-product of relevance ranking, which exacerbates exposure concentration and long-tail under-representation. We break this user-centric allocation of agency with a Tri-party LLM-agent Recommendation framework (TriRec). Responsibility is split deliberately: Stage 1 has each item generate self-promotion conditioned on the target user, which lowers cold-start barriers, while the exposure budget stays with the platform, whose Stage 2 sequential re-ranker balances relevance, item utility, and exposure fairness. On four public datasets TriRec improves accuracy, fairness, and item-level utility, with the accuracy gain significant on three of the four. A three-arm ablation at 50 candidates separates two levels of the mechanism on items that received no exposure during training: self-promotion drives the accuracy gain, and conditioning it on the target user adds further exposure, together raising these items' share of top-ranked exposure by 43.6% relative. Restricting promotions to catalogue-verifiable attributes cuts strong exaggeration to 0.5%/2.0% on two datasets while retaining 88.6%/92.0% of the accuracy gain. Our code is available at this https URL.
From: Yaxin Gong [view email]
[v1]
Wed, 11 Mar 2026 11:40:13 UTC (386 KB)
[v2]
Wed, 20 May 2026 15:21:58 UTC (683 KB)
[v3]
Tue, 25 Aug 2026 12:49:47 UTC (310 KB)
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