2020/09/30 by Walid Bendada, Guillaume Salha, Théo Bontempelli · 1 citation
Computer Science · Mathematics · #cs.LG #stat.ML
paper · pdf · doi:10.1145/3383313.3412217
14th ACM Conference on Recommender Systems (RecSys 2020, Best Short Paper Candidate)
arxiv created 2020/09/30 · arxiv updated 2020/10/01
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.