[Submitted on 16 Sep 2026]
Title:Bootstrapping Conversational Recommendation Agents At Spotify: Synthetic Data Generation and Self-Improvement Loops
View a PDF of the paper titled Bootstrapping Conversational Recommendation Agents At Spotify: Synthetic Data Generation and Self-Improvement Loops, by Enrico Palumbo and 17 other authors
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Abstract:Conversational recommendation agents are a new paradigm for content discovery, enabling users to express complex intents through natural language (e.g., "recommend Italian indie artists I haven't heard before"). A central challenge in building such agents is optimizing agent planning -- deciding how to select, sequence, and invoke tools -- particularly in cold-start settings where real user interactions are not yet available. We introduce a pipeline for multi-turn synthetic data generation and a self-improvement loop to address this challenge. The synthetic data pipeline transforms single-turn prompts into realistic multi-turn conversations, enabling systematic evaluation before launch. The self-improvement loop combines variance-based contrastive optimization with iterative refinement through a coding agent, automatically identifying and fixing planning and tool-use errors. Our approach improves quality by +8% on top of a highly optimized manual prompt. The system has been productionized and significantly accelerated iteration cycles for the launch of a conversational recommendation agent at Spotify. Online A/B tests demonstrate its effectiveness, with +14% user listening, +5% increase in weekly active users, and a 5% reduction in skip rate compared to a prior experience supporting only session refinement. This work provides a practical framework for accelerating the development of conversational recommendation agents in industry.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
Cite as: arXiv:2609.30297 [cs.CL]
(or arXiv:2609.30297v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.30297
arXiv-issued DOI via DataCite
Journal reference: RecSys 2026
Related DOI:
https://doi.org/10.1145/3773078.3831910
DOI(s) linking to related resources
Submission history
From: Enrico Palumbo [view email] [v1] Wed, 16 Sep 2026 14:48:02 UTC (742 KB)
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