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Online evaluation methods for information retrieval use implicit signals such as clicks from users to infer preferences between rankers. A highly sensitive way of inferring these preferences is through interleaved comparisons. Recently, interleaved comparisons methods that allow for simultaneous evaluation of more than two rankers have been introduced.(More)
Modern search systems are based on dozens or even hundreds of ranking features. The dueling bandit gradient descent (DBGD) algorithm has been shown to effectively learn combinations of these features solely from user interactions. DBGD explores the search space by comparing a possibly improved ranker to the current production ranker. To this end, it uses(More)
Online learning to rank methods aim to optimize ranking models based on user interactions. The dueling bandit gradient descent (DBGD) algorithm is able to effectively optimize linear ranking models solely from user interactions. We propose an extension of DBGD, called probabilistic multileave gradient descent (P-MGD) that builds on probabilistic multileave,(More)
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