Evaluating the Robustness of Learning from Implicit Feedback

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This 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.
8 pages, Presented at ICML Workshop on Learning In Web Search, 2005

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Machine Learning, Information Retrieval, H.3.3

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http://salesiana.dossiersoluciones.com/handle/123456789/111082

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cs/0605036
 http://arxiv.org/abs/cs/0605036

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arXiv