A Vrbo team used daily Thompson sampling to rank four recommendation models by click-through rate, but the A/B validation they report is for a previous campaign's winner, not the current one.
SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
Motivated by cognitive radio networks, we consider the stochastic multiplayer multi-armed bandit problem, where several players pull arms simultaneously and collisions occur if one of them is pulled by several players at the same stage. We present a decentralized algorithm that achieves the same performance as a centralized one, contradicting the existing lower bounds for that problem. This is possible by "hacking" the standard model by constructing a communication protocol between players that deliberately enforces collisions, allowing them to share their information at a negligible cost. This motivates the introduction of a more appropriate dynamic setting without sensing, where similar communication protocols are no longer possible. However, we show that the logarithmic growth of the regret is still achievable for this model with a new algorithm.
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2019 1verdicts
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Accelerated learning from recommender systems using multi-armed bandit
A Vrbo team used daily Thompson sampling to rank four recommendation models by click-through rate, but the A/B validation they report is for a previous campaign's winner, not the current one.