Conversational Recommendation Agents: Enhancing User Discovery at Spotify

Paper: Bootstrapping Conversational Recommendation Agents At Spotify: Synthetic Data Generation and Self-Improvement Loops

Authors: Enrico Palumbo, Alexandre Tamborrino, Victor Ode, Ben Lacker, Adri`a Casas Escoda, Jeremy Hopple, Marcus Better, James Leoni, Hugo Galv~ao, Hugues Bouchard, Mounia Lalmas, Jos'e Luis Redondo Garc'ia, Abenezer Abebe, Ann Clifton, Anton Blomberg, Henrik Lindstr"om, Dani Doro, Christine Doig Cardet

Executive Summary

Recent research focuses on enhancing conversational recommendation agents, which help users discover content through natural language interactions. An example of such a request might be, "recommend Italian indie artists I haven't heard before." A key challenge in developing these agents is optimizing how they plan and make decisions—specifically, how they select, sequence, and use various tools—especially in situations where user data is limited or nonexistent, known as cold-start settings.

To tackle this issue, the researchers developed a two-part solution: a pipeline for generating synthetic multi-turn conversation data and a self-improvement mechanism. The synthetic data pipeline takes simple, single-turn prompts and expands them into realistic, multi-turn conversations. This allows for thorough evaluation of the agent's performance before it is launched, ensuring a more robust user experience from the start.

The self-improvement mechanism focuses on refining the agent's planning capabilities. It employs a technique called variance-based contrastive optimization, which works in conjunction with an iterative coding agent. This system identifies and corrects errors in the agent's planning and tool usage automatically, enhancing its effectiveness.

In practical applications, this approach led to a notable improvement in the conversational recommendation agent at Spotify. The system saw an increase of 8% in quality metrics compared to a well-optimized manual prompt. Furthermore, through online A/B testing, it demonstrated significant user engagement enhancements: a 14% increase in user listening, a 5% boost in weekly active users, and a 5% decrease in the skip rate, compared to previous experiences that only focused on refining individual sessions.

The framework outlined in this research provides a practical method for companies to expedite the development of conversational recommendation agents. By leveraging synthetic data generation and automated self-improvement, businesses may be able to create more effective and engaging user experiences in content discovery. This research illustrates a viable path for organizations looking to enhance their recommendation systems and could influence future developments in the field.

Academic 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.