Overview and Analysis of the RecSys Challenge 2026: Conversational Music Recommendation
This study addresses the challenge of jointly modeling music recommendation and response generation in multi-turn dialogues by proposing a retrieve-rerank-generate framework. Methodologically, it integrates heterogeneous candidate sources, learning-based reranking, and natural language generation techniques. The system design incorporates robust principles such as cold-start grounding, intent detection, and full-context modeling. Through systematic experiments evaluating 16 systems across varying users and contexts, this work reveals significant performance disparities and exposes critical limitations in existing benchmarks. Ultimately, this research provides both theoretical foundations and practical guidance for the architectural design and evaluation of conversational recommender systems.