To automatically extract user and content groups from streaming data, we employ "co-clustering", an unsupervised learning technique to simultaneously extract ...

May 1, 2019 · We aim at improving upon a current bandit algorithm used to select which playlists to display to users on the home of Spotify. We explore ...

To automatically extract user and content groups from streaming data, we employ ”co-clustering”, an unsupervised learning technique to simultaneously extract ...

This work proposes co-clustered based reward functions, an unsupervised learning technique to simultaneously extract clusters of rows and columns from a ...

Deriving User- and Content-specific Rewards for Contextual Bandits. Dragone, P., Mehrotra, R., & Lalmas, M. In Proceedings of the International World Wide ...

Aug 6, 2024 · LinUCB is an algorithm that, when given a context, will select an article the user is likely to click. However, the articles need not be actual articles.

Apr 18, 2024 · The contextual bandit problem presents a unique machine learning challenge, blending elements of both exploration and exploitation within decision-making ...

Jun 12, 2023 · We study the fundamental limits of learning in contextual bandits, where a learner's rewards depend on their actions and a known context, which ...

The contextual bandit algorithm balances exploration and exploitation to maximize the expected total reward. Equivalently, the algorithm aims to minimize the ...

In contextual bandits, an algorithm must choose actions given observed contexts, learning from a reward signal that is ob- served only for the action chosen.