Diversifying Music Recommendations

10/02/2018

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We compare submodular and Jaccard methods to diversify Amazon Music recommendations. Submodularity significantly improves recommendation quality and user engagement. Unlike the Jaccard method, our submodular approach incorporates item relevance score within its optimization function, and produces a relevant and uniformly diverse set.

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Diversifying Music Recommendations

Common Artist Music Assistance

Video-to-Music Recommendation using Temporal Alignment of Segments

Generating Synthetic Data for Conversational Music Recommendation Using Random Walks and Language Models

Evaluating Music Recommendations with Binary Feedback for Multiple Stakeholders

Tracing Affordance and Item Adoption on Music Streaming Platforms

Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study

When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces

Diversifying Music Recommendations

Related Research

Common Artist Music Assistance

Video-to-Music Recommendation using Temporal Alignment of Segments

Generating Synthetic Data for Conversational Music Recommendation Using Random Walks and Language Models

Evaluating Music Recommendations with Binary Feedback for Multiple Stakeholders

Tracing Affordance and Item Adoption on Music Streaming Platforms

Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study

When the Umpire is also a Player: Bias in Private Label Product Recommendations on E-commerce Marketplaces