# Carousel Personalization in Music Streaming Apps with Contextual Bandits

arXiv: https://arxiv.org/abs/2009.06546 · HTML: https://mlsanity.com/paper/2009.06546/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 26 of 26)

- [A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps](https://mlsanity.com/paper/2106.03819/) (2021)
- [Explainability in Music Recommender Systems](https://mlsanity.com/paper/2201.10528/) (2022)
- [On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendation](https://mlsanity.com/paper/2307.15053/) (2023)
- [Optimal Baseline Corrections for Off-Policy Contextual Bandits](https://mlsanity.com/paper/2405.05736/) (2024)
- [Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders](https://mlsanity.com/paper/2108.01053/) (2021)
- [Flow Moods: Recommending Music by Moods on Deezer](https://mlsanity.com/paper/2207.11229/) (2022)
- [Multi-Objective Recommendation via Multivariate Policy Learning](https://mlsanity.com/paper/2405.02141/) (2024)
- [Practical Bandits: An Industry Perspective](https://mlsanity.com/paper/2302.01223/) (2023)
- [A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services](https://mlsanity.com/paper/2304.09061/) (2023)
- [Track Mix Generation on Music Streaming Services using Transformers](https://mlsanity.com/paper/2307.03045/) (2023)
- [Let's Get It Started: Fostering the Discoverability of New Releases on Deezer](https://mlsanity.com/paper/2401.02827/) (2024)
- [Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Sampling](https://mlsanity.com/paper/2507.00518/) (2025)
- [Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation](https://mlsanity.com/paper/2209.08642/) (2022)
- [Bandit Learning to Rank with Position-Based Click Models: Personalized and Equal Treatments](https://mlsanity.com/paper/2311.04528/) (2023)
- [Clustering Context in Off-Policy Evaluation](https://mlsanity.com/paper/2502.21304/) (2025)
- [Page-level Optimization of e-Commerce Item Recommendations](https://mlsanity.com/paper/2108.05891/) (2021)
- [Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model](https://mlsanity.com/paper/2210.09512/) (2022)
- [Music Playlist Captioning at Scale with Large Language Models](https://mlsanity.com/paper/2606.22460/) (2026)
- [A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels](https://mlsanity.com/paper/2105.06275/) (2021)
- [Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels](https://mlsanity.com/paper/2105.07062/) (2021)
- [Bounded (O(1)) Regret Recommendation Learning via Synthetic Controls Oracle](https://mlsanity.com/paper/2301.12571/) (2023)
- [Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience](https://mlsanity.com/paper/2309.14046/) (2023)
- [Efficient and Interpretable Bandit Algorithms](https://mlsanity.com/paper/2310.14751/) (2023)
- [Counterfactual Inference under Thompson Sampling](https://mlsanity.com/paper/2504.08773/) (2025)
- [Recycling History: Efficient Recommendations from Contextual Dueling Bandits](https://mlsanity.com/paper/2508.18841/) (2025)
- [Ranking Bandits for Carousel Interfaces with Observable Browsing Depth](https://mlsanity.com/paper/2610.05220/) (2026)

## Abstract (arXiv)

Media services providers, such as music streaming platforms, frequently leverage swipeable carousels to recommend personalized content to their users. However, selecting the most relevant items (albums, artists, playlists...) to display in these carousels is a challenging task, as items are numerous and as users have different preferences. In this paper, we model carousel personalization as a contextual multi-armed bandit problem with multiple plays, cascade-based updates and delayed batch feedback. We empirically show the effectiveness of our framework at capturing characteristics of real-world carousels by addressing a large-scale playlist recommendation task on a global music streaming mobile app. Along with this paper, we publicly release industrial data from our experiments, as well as an open-source environment to simulate comparable carousel personalization learning problems.