Shifting Consumption towards Diverse content via Reinforcement Learning | Spotify Research

Categories: Research, Automation / Agents, Data Analysis | Pricing: Enterprise | Official Website ↗

This research details a reinforcement learning-based sequencer designed to diversify music recommendations on streaming platforms, balancing user satisfaction with content exploration.

This research from Spotify explores how to shift music consumption towards more diverse content using a reinforcement learning (RL) based sequencer. The goal is to address issues like filter bubbles and popularity bias, promoting a healthier consumption pattern by encouraging users to discover less popular content and content outside their typical tastes. The study formalizes diversity around two factors: taste similarity and popularity, aiming to recommend music sufficiently different from user preferences and to include less popular tracks. The paper investigates the relationship between diversity, relevance, and user satisfaction, finding that while reducing user-track similarity can impact satisfaction, popularity can be reduced without significant negative impact. This suggests an opportunity to recommend more diverse content without compromising user experience. The core of the solution is an RL-based sequencer that learns a policy to maximize a reward function, jointly considering user satisfaction and diversity goals when generating track recommendations. The RL sequencer is a sampling-based system that selects tracks based on previous recommendations. It incorporates neural components like a session summarizer, dynamic user embedding, and a feedback-aware ranking module to build rich representations of user state and tracks. The system defines a reward function that guides the policy to sample tracks that balance user engagement with the desired level of content diversity.

Key Features

Reinforcement Learning based track sequencer
Quantification of diversity (taste similarity, popularity)
Neural components for session summarization
Dynamic user embedding generation
Feedback-aware ranking module
Reward formulation for diversity and satisfaction
Track sampling policy

Pros

Addresses filter bubbles and popularity bias in recommendations
Aims to increase content diversity without negatively impacting user satisfaction
Utilizes advanced reinforcement learning techniques for sequencing
Incorporates dynamic user feedback and session context
Contributes to long-term user engagement and platform sustainability

Cons

This is a research paper, not a commercial product
Implementation details are complex, requiring specialized ML expertise
Focuses on music, may not be directly applicable to other content types without adaptation
Requires extensive data on user sessions and track features
Balancing diversity and immediate relevance can be challenging

Use Cases

Developing more diverse music recommendation algorithms
Improving user engagement and retention on streaming platforms
Mitigating algorithmic biases like filter bubbles and popularity bias
Personalizing content discovery experiences

Best For

AI researchers
Recommendation system developers
Music streaming platforms
Data scientists interested in reinforcement learning applications

Platforms: Web

Watch demo on YouTube ↗

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