RecSys Challenge 2026: Conversational Music Recommendation

Abstract

The RecSys Challenge 2026 focuses on the evolving landscape of music discovery, where static recommendation lists are being replaced by dynamic, conversational interactions. As users increasingly interact with AI through natural language, there is a critical need for systems that can seamlessly integrate Natural Language Understanding (NLU) with high-precision Recommender Systems (RecSys). The challenge pushes the boundaries of how AI understands nuanced user preferences, explores musical tastes through dialogue, and provides contextually relevant track recommendations. The participants are asked to tackle the complexities of multi-turn preference elicitation, using the TalkPlayData-Challenge dataset, a large-scale conversation resource generated through an advanced agentic pipeline. The dataset features LLM-generated multi-turn dialogues paired with music metadata and user-item interaction data derived from publicly available research datasets. This challenge bridges the NLP and RecSys communities, driving innovation in interactive, personalized music information retrieval.