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Thompson Sampling Algorithms for Cascading Bandits
[article]
2021
arXiv
pre-print
Motivated by the pressing need for efficient optimization in online recommender systems, we revisit the cascading bandit model proposed by Kveton et al. (2015). While Thompson sampling (TS) algorithms have been shown to be empirically superior to Upper Confidence Bound (UCB) algorithms for cascading bandits, theoretical guarantees are only known for the latter. In this paper, we first provide a problem-dependent upper bound on the regret of a TS algorithm with Beta-Bernoulli updates; this upper
arXiv:1810.01187v4
fatcat:o6ptav6banhtdao6wx77a2gsjm