Personalization via Probabilistic Adaptation - Sontag, David; Collins-Thompson, Kevyn; Bennett, Paul; White, Ryen W.; Dumais, Susan; von Billerbeck, Bodo (NIPS 2013 Workshop on Personalization (An extended abstract of work that first appeared in WSDM 2012), 2013)
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 specific user for a search query, and show how to learn these profiles from a user’s long-term search history. Our algorithm for computing the personalized ranking is simple and has little computational overhead. We evaluate our personalization approach using historical search data from thousands of users of a major Web search engine.