Probabilistic models for personalizing web search

  title={Probabilistic models for personalizing web search},
  author={David A Sontag and Kevyn Collins-Thompson and Paul N. Bennett and Ryen W. White and Susan T. Dumais and Bodo Billerbeck},
We present a new approach for personalizing Web search results to a specific user. Ranking functions for Web search engines are typically trained by machine learning algorithms using either direct human relevance judgments or indirect judgments obtained from click-through data from millions of users. The rankings are thus optimized to this generic population of users, not to any specific user. We propose a generative model of relevance which can be used to infer the relevance of a document to a… CONTINUE READING
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