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"Learning to rank for information retrieval from user interactions" by K. Hofmann, S. Whiteson, A. Schuth, and M. de Rijke with Martin Vesely as coordinator
ACM SIGWEB Newsletter
In this article we give an overview of our recent work on online learning to rank for information retrieval (IR). This work addresses IR from a reinforcement learning (RL) point of view, with the aim to enable systems that can learn directly from interactions with their users. Learning directly from user interactions is difficult for several reasons. First, user interactions are hard to interpret as feedback for learning because it is usually biased and noisy. Second, the system can onlydoi:10.1145/2591453.2591458 fatcat:pcsnnsvienemjbzn2vrvjovkim