Feb 25, 2019 · Abstract:We generalize the setting of online clustering of bandits by allowing non-uniform distribution over user frequencies.
These online clustering of bandit algorithms adaptively learn the clustering structure over users based on the collaborative recommendation results to gather.
These online clustering of bandit algorithms adaptively learn the clustering structure over users based on the collaborative recommendation results to gather.
We generalize the setting of online clustering of bandits by allowing non-uniform distribution over user frequencies. A more efficient algorithm is proposed ...
A more efficient algorithm is proposed with simple set structures to represent clusters and it is proved a regret bound for the new algorithm which is free ...
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Aug 1, 2019 · We generalize the setting of online clustering of bandits by allowing non-uniform distribution over user frequencies.
A more efficient algorithm is proposed with simple set structures to represent clusters. We prove a regret bound for the new algorithm which is free of the ...
We introduce a novel algorithmic approach to content recommendation based on adaptive clustering of exploration-exploitation (“bandit") strategies.
Missing: Improved | Show results with:Improved
Sep 25, 2024 · We introduce improved algorithms for online clustering of bandits by incorporating a novel exploration phase, resulting in better regret upper bound.
A novel algorithmic approach to content recommendation based on adaptive clustering of exploration-exploitation "bandit" strategies that shows a significant ...