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Conversational contextual bandits elicit user preferences by occasionallyquerying for explicit feedback on key-terms to accelerate learning. However,there are aspects of existing approaches which limit their performance. First,information gained from key-term-level conversations and arm-levelrecommendations is not appropriately incorporated to speed up learning. Second,it is important to ask explorative key-terms to quickly elicit the user'spotential interests in various domains to accelerate the convergence of userpreference estimation, which has never been considered in existing works. Totackle these issues, we first propose ``ConLinUCB", a general framework forconversational bandits with better information incorporation, combiningarm-level and key-term-level feedback to estimate user preference in one stepat each time. Based on this framework, we further design two bandit algorithmswith explorative key-term selection strategies, ConLinUCB-BS and ConLinUCB-MCR.We prove tighter regret upper bounds of our proposed algorithms. Particularly,ConLinUCB-BS achieves a regret bound of $O(d\sqrt{T\log T})$, better than theprevious result $O(d\sqrt{T}\log T)$. Extensive experiments on synthetic andreal-world data show significant advantages of our algorithms in learningaccuracy (up to 54\% improvement) and computational efficiency (up to 72\%improvement), compared to the classic ConUCB algorithm, showing the potentialbenefit to recommender systems.

Efficient Explorative Key-term Selection Strategies for Conversational Contextual Bandits