Reinforcement Learning Ranker for Diverse Media Recommendations
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Solution Overview
Problem
Traditional music streaming recommendation algorithms fail to incorporate diversity in track selection, neglecting the sequential nature of music consumption and user behavior, leading to a lack of exposure to diverse content and reinforcing popularity biases.
Innovation Solution
A reinforcement learning ranker is employed to generate recommendations by maximizing a reward function that balances relevance and diversity, using track and user features from previous sessions to select tracks that are both liked by the user and dissimilar to recent recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation algorithms are used to maximize user satisfaction, then relevance scores are improved, but diversity of content is reduced
Solution Approach 1:
The patent changes the objective function parameters by introducing a diversity term alongside the relevance term in the reward function. This transforms the single-parameter optimization (relevance only) into a multi-parameter optimization (relevance + diversity), allowing the system to balance both goals simultaneously through the reward function formulation: R(t, s) = r(t, u) - c + α * d(t, s) * r(t, u)
2Measurement precision
If popularity-based recommendations are used, then user satisfaction is improved, but fairness among items is reduced
Solution Approach 1:
The patent implements feedback mechanisms where user interactions (skips, plays, session endings) are fed back into the reinforcement learning model to learn from actual user behavior rather than relying on popularity metrics. This feedback loop allows the system to adapt to individual user preferences and promote fairer distribution of recommendations across different items, including less popular ones that users may actually enjoy
3Adaptability or versatility
If reinforcement learning with diversity reward is implemented, then diversity is improved, but computational complexity increases
Solution Approach 1:
The patent pre-computes track embeddings using content-based features (acoustic properties, lyrics, metadata) before the recommendation process. These pre-computed embeddings are then used to efficiently calculate diversity scores during actual recommendations, avoiding the need for complex real-time computations. This preliminary action significantly reduces the computational burden during the recommendation phase while maintaining diversity capabilities
Data Source
AI summary
A reinforcement learning ranker can take into account previously-recommended media content items to produce a ranked list of media content items to recommend next. The ranker finds a policy that gives the probability of sampling a media content item given a state. The policy is learned such that it maximizes a reward. A reward function associated with the media content item can be defined with respect to whether the user finds the media content item relevant (likelihood that the user will like the media content item) and a diversity score of the media content item.