Carousel Personalization in Music Streaming Apps with Contextual Bandits

Abstract

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.

arXiv comment14th ACM Conference on Recommender Systems (RecSys 2020, Best Short Paper Candidate)

References in corpus (4)

• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• A Contextual-Bandit Approach to Personalized News Article Recommendation
• Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
• Current Challenges and Visions in Music Recommender Systems Research

Cited by in corpus (6)

• Explainability in Music Recommender Systems
• User Simulation for Evaluating Information Access Systems
• A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services
• Optimal Baseline Corrections for Off-Policy Contextual Bandits
• RecGaze: The First Eye Tracking and User Interaction Dataset for Carousel Interfaces
• Rethinking Click Models in Light of Carousel Interfaces: Theory-Based Categorization and Design of Click Models