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

2026-07-24

AI Summary

This work addresses the limitation in traditional conversational recommender systems where tightly coupling retrieval and response generation weakens entity-level signals during dialogue intent evolution, thereby undermining explanation credibility. To overcome this, the study introduces the first explicit decoupling of retrieval and generation in conversational music recommendation. The retrieval module integrates lexical and dense representations, employs a fine-tuned Qwen-8B adapter for task-adaptive pooling, and refines candidates via LightGBM calibration. The generation module adopts an evidence-anchored Propose-Allocate-Select (PAS) framework to structurally leverage retrieved evidence for producing interpretable responses. This approach substantially enhances explanation reliability, achieving third place overall and second in explanation quality in the ACM RecSys Challenge 2026 Blind-B track.

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.

Problem

• conversational recommendation
• evidence-grounded response
• retrieval-generation decoupling
• explanation credibility
• entity cues

Innovation

• evidence-grounded recommendation
• retrieval-generation decoupling
• propose-assign-select framework
• hybrid lexical-dense retrieval
• conversational music recommendation