Max-Utility Based Arm Selection Strategy For Sequential Query Recommendations

arXiv:2108.13810v1 Announce Type: cross
Abstract: We consider the query recommendation problem in closed loop interactive learning settings like online information gathering and exploratory analytics. The problem can be naturally modelled using the Multi-Armed Bandits (MAB) framework with countably many arms. The standard MAB algorithms for countably many arms begin with selecting a random set of candidate arms and then applying standard MAB algorithms, e.g., UCB, on this candidate set downstream. We show that such a selection strategy often results in …

arm arxiv cs.ai cs.lg max query recommendations stat.ml strategy type utility