Talk Less, Rank Better: A Decoupled Pipeline for Conversational Music Recommendation

This paper describes the solution submitted by team Hallucinated to the ACM RecSys Challenge 2026, based on the TalkPlayData conversational music recommendation dataset. The Challenge poses a dual task: at every turn of a multi-turn dialogue, a system must both (i) rank the most relevant music tracks from the catalogue and (ii) generate a natural language assistant response justifying the recommendation. We address the ranking task with a classic multi-stage pipeline that combines a diverse pool of candidate generators, Reciprocal Rank Fusion, and a learned XGBoost reranker, with validation subsets shaped to resemble the Blind data and tuning objectives that favour stable performance under distribution shift. The response generation task is handled by a multi-stage local Large Language Model pipeline, optimised for lexical diversity. The complete code and data are available at https://github.com/remaplab/recsys-challenge-2026-music-crs for reproducibility.