Bootstrapping Conversational Recommendation Agents At Spotify: Synthetic Data Generation and Self-Improvement Loops
TL;DR
This work provides a practical framework for accelerating the development of conversational recommendation agents in industry, addressing challenges that are becoming increasingly central to recommender systems and agentic applications as natural-language interfaces reach widespread adoption.
Abstract
Conversational recommendation agents are emerging as 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” or “explain why they fit my taste”). A central challenge in building such agents is optimizing agent planning, i.e., deciding how to select, sequence, and invoke tools. This challenge is particularly acute 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 the lack of interaction data and the difficulty of optimizing agent planning in cold-start settings. The synthetic data pipeline transforms single-turn prompts into realistic multi-turn user–agent conversations, enabling systematic evaluation of conversational capabilities before launch. The self-improvement loop then uses this data and evaluation feedback to combine variance-based contrastive optimization with iterative refinement through a coding agent, allowing the system to automatically identify and fix planning and tool-use errors. Our approach provides fine-grained insights into conversational capabilities, uncovers issues before deployment, and improves quality by +8% on top of a highly optimized manual prompt, automatically resolving several planning and tool-use errors. The system has been productionized and significantly accelerated iteration cycles for the launch of a conversational recommendation agent at Spotify.1. 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 that only supports session refinement. Overall, this work provides a practical framework for accelerating the development of conversational recommendation agents in industry, addressing challenges that are becoming increasingly central to recommender systems and agentic applications as natural-language interfaces reach widespread adoption.
