Two Views, One Voice: Evidence-Grounded Conversational Music Recommendation

Abstract:Traditional conversational recommenders entangle retrieval and response generation within a single text interface, so exact entity cues fade as the dialogue's intent evolves, which compromises explanation credibility. We address this within the ACM RecSys Challenge 2026, which mandates both top-20 ranking and evidence-grounded response generation. This paper presents the third-place solution by team "swyoo" for the Blind-B industry track. We decouple retrieval and response into separate pipelines connected strictly via ranked tracks and metadata. Retrieval combines a hybrid lexical-dense pool for exact matching with a task-adapted pool driven by fine-tuned Qwen 8B adapters. Candidates are calibrated via LightGBM, then routed to an evidence-grounded propose-assign-select (PAS) framework to structure responses. This system also ranked second on the explanation-quality leaderboard in the final blind evaluation. Our findings demonstrate that: (i) isolating retrieval and response preserves both catalog cues and fluid intent; (ii) structuring generation via explicit evidence assignment is key to this near-best-in-class explanation reliability.

5 pages, 4 figures, 5 tables. 4th-place solution (team swyoo) in the Blind-B industry track of the ACM RecSys Challenge 2026

Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2607.24846 [cs.IR]

(or arXiv:2607.24846v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2607.24846 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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