Evaluating the Robustness of Learning from Implicit Feedback

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dc.creatorRadlinski, Filip
dc.creatorJoachims, Thorsten
dc.date2006-05-08
dc.date.accessioned2026-07-07T07:09:29Z
dc.date.available2026-07-07T07:09:29Z
dc.descriptionThis paper evaluates the robustness of learning from implicit feedback in web search. In particular, we create a model of user behavior by drawing upon user studies in laboratory and real-world settings. The model is used to understand the effect of user behavior on the performance of a learning algorithm for ranked retrieval. We explore a wide range of possible user behaviors and find that learning from implicit feedback can be surprisingly robust. This complements previous results that demonstrated our algorithm's effectiveness in a real-world search engine application.
dc.description8 pages, Presented at ICML Workshop on Learning In Web Search, 2005
dc.identifierhttps://arxiv.org/abs/cs/0605036
dc.identifierhttp://arxiv.org/abs/cs/0605036
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/111082
dc.subjectMachine Learning
dc.subjectInformation Retrieval
dc.subjectH.3.3
dc.titleEvaluating the Robustness of Learning from Implicit Feedback
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