Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending
This addresses a practical problem for media streaming services needing to rank diverse content types like music and podcasts, but it is incremental as it builds on existing learning-to-rank algorithms.
The paper tackles the challenge of ranking items across different content types in media streaming services, where traditional learning-to-rank algorithms struggle due to varying user engagement patterns. It introduces multinomial blending, a simple method that improves ranking quality and other industry-relevant aspects, as demonstrated in an A/B test on Amazon Music.
An increasing number of media streaming services have expanded their offerings to include entities of multiple content types. For instance, audio streaming services that started by offering music only, now also offer podcasts, merchandise items, and videos. Ranking items across different content types into a single slate poses a significant challenge for traditional learning-to-rank (LTR) algorithms due to differing user engagement patterns for different content types. We explore a simple method for cross-content-type ranking, called multinomial blending (MB), which can be used in conjunction with most existing LTR algorithms. We compare MB to existing baselines not only in terms of ranking quality but also from other industry-relevant perspectives such as interpretability, ease-of-use, and stability in dynamic environments with changing user behavior and ranking model retraining. Finally, we report the results of an A/B test from an Amazon Music ranking use-case.