# Ranking Across Different Content Types: The Robust Beauty of Multinomial Blending

arXiv: https://arxiv.org/abs/2408.09168 · HTML: https://mlsanity.com/paper/2408.09168/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 17 Aug 2024.

## Builds on (most cited first, 6 of 6)

- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness](https://mlsanity.com/paper/2105.00855/) (2021)
- [Offline Evaluation of Ranking Policies with Click Models](https://mlsanity.com/paper/1804.10488/) (2018)
- [Improving Deep Learning For Airbnb Search](https://mlsanity.com/paper/2002.05515/) (2020)
- [Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking](https://mlsanity.com/paper/2206.07247/) (2022)
- [Unbiased Offline Evaluation for Learning to Rank with Business Rules](https://mlsanity.com/paper/2311.01828/) (2023)

## Built on by (most cited first, 2 of 2)

- [Calibrated Recommendations with Contextual Bandits](https://mlsanity.com/paper/2509.05460/) (2025)
- [DenseRec: Revisiting Dense Content Embeddings for Sequential Transformer-based Recommendation](https://mlsanity.com/paper/2508.18442/) (2025)

## Abstract (arXiv)

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.