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Large-scale online recommendation systems must facilitate the allocation of alimited number of items among competing users while learning their preferencesfrom user feedback. As a principled way of incorporating market constraints anduser incentives in the design, we consider our objectives to be two-fold:maximal social welfare with minimal instability. To maximize social welfare,our proposed framework enhances the quality of recommendations by exploringallocations that optimistically maximize the rewards. To minimize instability,a measure of users' incentives to deviate from recommended allocations, thealgorithm prices the items based on a scheme derived from the Walrasianequilibria. Though it is known that these equilibria yield stable prices formarkets with known user preferences, our approach accounts for the inherentuncertainty in the preferences and further ensures that the users accept theirrecommendations under offered prices. To the best of our knowledge, ourapproach is the first to integrate techniques from combinatorial bandits,optimal resource allocation, and collaborative filtering to obtain an algorithmthat achieves sub-linear social welfare regret as well as sub-linearinstability. Empirical studies on synthetic and real-world data alsodemonstrate the efficacy of our strategy compared to approaches that do notfully incorporate all these aspects.

Interactive Learning with Pricing for Optimal and Stable Allocations in Markets