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Fast and Reliable Online Learning to Rank for Information Retrieval
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Online learning to rank for information retrieval (IR) holds promise for allowing the development of “self-learning search engines” that can automatically adjust to their users. With the large amount of e.g., click data that can be collected in web search settings, such techniques could enable highly scalable ranking optimization. However, feedback obtained from user interactions is noisy, and developing approaches that can learn from this feedback quickly and reliably is a major challenge.
In this talk I will present my recent work, which addresses the challenges posed by learning from natural user interactions. First, I will detail a new method, called Probabilistic Interleave, for inferring user preferences from users’ clicks on search results. I show that this method allows unbiased and fine-grained ranker comparison using noisy click data, and that this is the first such method that allows the effective reuse of historical data (i.e., collected for previous comparisons) to infer information about new rankers. Second, I show that Probabilistic Interleave enables new online learning to rank approaches that can reuse historical interaction data to speed up learning by several orders of magnitude, especially under high levels of noise in user feedback. I conclude with an outlook on research directions in online learning to rank for IR, that are opened up by our results.
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