308 research outputs found
Hierarchical Exploration for Accelerating Contextual Bandits
Contextual bandit learning is an increasingly popular approach to optimizing
recommender systems via user feedback, but can be slow to converge in practice
due to the need for exploring a large feature space. In this paper, we propose
a coarse-to-fine hierarchical approach for encoding prior knowledge that
drastically reduces the amount of exploration required. Intuitively, user
preferences can be reasonably embedded in a coarse low-dimensional feature
space that can be explored efficiently, requiring exploration in the
high-dimensional space only as necessary. We introduce a bandit algorithm that
explores within this coarse-to-fine spectrum, and prove performance guarantees
that depend on how well the coarse space captures the user's preferences. We
demonstrate substantial improvement over conventional bandit algorithms through
extensive simulation as well as a live user study in the setting of
personalized news recommendation.Comment: Appears in Proceedings of the 29th International Conference on
Machine Learning (ICML 2012
Let's Get It Started: Fostering the Discoverability of New Releases on Deezer
This paper presents our recent initiatives to foster the discoverability of
new releases on the music streaming service Deezer. After introducing our
search and recommendation features dedicated to new releases, we outline our
shift from editorial to personalized release suggestions using cold start
embeddings and contextual bandits. Backed by online experiments, we discuss the
advantages of this shift in terms of recommendation quality and exposure of new
releases on the service.Comment: Accepted for presentation as an "Industry Talk" at the 46th European
Conference on Information Retrieval (ECIR 2024
Carousel Personalization in Music Streaming Apps with Contextual Bandits
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.Comment: 14th ACM Conference on Recommender Systems (RecSys 2020, Best Short
Paper Candidate
- …