| Document Date: 2012-06-14 12:13:58
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City Portland / / Company MIT Press / Kendall / Yahoo! / Microsoft / / Country United States / / Currency USD / / / Facility Computer Science University of Maryland College Park / / IndustryTerm grid search / Web applications / Web data sets / activity-based search context / well-developed machine learning algorithms / optimization algorithm / Web search evaluation / ent applications / variance recommendation algorithms / Web search queries / search result / search location / recommender systems / web relevance measure / re-rank web search results / risk-aware algorithms / personalized search strategies / search settings / search personalization / risk-sensitive algorithm / web search environments / learning to rank algorithms / web search results / search result quality / retrieval algorithm / web search / learning-to-rank algorithms / retrieval algorithms / search engine / search results / learning algorithm / learning algorithms / / Organization MIT / University of Maryland College Park / U.S. Securities and Exchange Commission / / Person Mm / N. Bennett Kevyn Collins-Thompson / Paul N. Bennett / Mb / Wang Paul / John Wiley / / / Position ranking model / reranking model for each query / model / Markov Random Field model for term dependencies / baseline model / / ProvinceOrState Oregon / / Technology alpha / risk-aware algorithms / retrieval algorithms / learning-to-rank algorithms / search engine / machine learning / well-developed machine learning algorithms / optimization algorithm / learning algorithm / retrieval algorithm / LambdaMart algorithm / high-variance recommendation algorithms / ranking algorithm / /
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Robust Ranking Models via Risk-Sensitive Optimization Lidan Wang Paul N. Bennett Kevyn Collins-Thompson