Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism

We propose Collab-Rec , a multi-agent framework designed to counteract popularity bias and enhance diversity in tourism recommendations. In our setting, three LLM-based agents ( Personalization , Popularity , and Sustainability ) generate city suggestions from complementary perspectives. A non-LLM moderator then merges and refines these proposals through iterative constrained refinement, ensuring each agent’s viewpoint is incorporated while penalizing spurious or repeated responses. Extensive offline experiments on European city queries using LLMs from different sizes and model families demonstrate that Collab-Rec enhances diversity and overall relevance compared to a single-agent baseline, surfacing lesser-visited locales that are often overlooked. This balanced, context-aware approach better reflects a broader range of user and system-level considerations, highlighting the potential of multi-stakeholder collaboration in LLM-driven recommender systems. Code, data, and other artifacts are available here: https://github.com/ashmibanerjee/collab-rec while the prompts used are included in the appendix.