[2508.15030] Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism

Computer Science > Artificial Intelligence arXiv:2508.15030 (cs) [Submitted on 20 Aug 2025 (v1), last revised 2 Mar 2026 (this version, v4)] Title:Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism Authors:Ashmi Banerjee, Adithi Satish, Fitri Nur Aisyah, Wolfgang Wörndl, Yashar Deldjoo View a PDF of the paper titled Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism, by Ashmi Banerjee and 4 other authors View PDF HTML (experimental) Abstract: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 via multi-round negotiation, ensuring each agent's viewpoint is incorporated while penalizing spurious or repeated responses. Extensive 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 addresses over-tourism and better aligns with user-provided constraints, highlighting the promise of multi-stakeholder collaboration in LLM-driven recommender systems. Code, data, and other artifacts are available...