arXiv:2508.15030v5 Announce Type: replace Abstract: We propose COLLAB-REC, a multi-agent framework designed to counteract popularity bias and improve diversity in tourism recommendations. In our setup, three LLM-based agents(Personalization, Popularity, and Sustainability) generate city suggestions from different perspectives. A non-LLM moderator then merges and refines these proposals through iterative constrained refinement, ensuring that each agent's viewpoint is represented while reducing spurious or repeated outputs. Extensive offline experiments on European city queries using LLMs of dif
The paper leverages recent advancements in large language models to address known limitations in recommendation systems, specifically popularity bias and diversity, which are pressing concerns as LLMs become ubiquitous.
Sophisticated readers will recognize this as an early indicator of how LLM-based agentic frameworks will move beyond simple query-response systems to tackle complex, multi-objective problems across various domains.
Recommendations can now be dynamically balanced by autonomous LLM agents with distinct perspectives, leading to more nuanced and potentially more equitable outputs compared to traditional, static algorithms.
- · AI agent developers
- · Tourism platforms
- · Consumers seeking diverse experiences
- · LLM providers
- · Traditional recommendation system companies
- · Static algorithm-based services
The immediate effect is improved, more diversified recommendations in sectors like tourism.
This framework could be generalized to balance diverse objectives in other decision-making systems, such as financial advice or content moderation.
The widespread adoption of multi-agent LLM systems with iterative refinement could fundamentally alter how complex problems are approached, decentralizing and specializing AI functions.
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Read at arXiv cs.AI