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Some commercial web search engines rely on sophisticated machine learning systems for ranking web documents. Due to very large collection sizes and tight constraints on query response times, online efficiency of these learning systems forms a bottleneck. An important problem in such systems is to speedup the ranking process without sacrificing much from the quality of results. In this paper, we propose optimization strategies that allow short-circuiting score computations in additive learningdoi:10.1145/1718487.1718538 dblp:conf/wsdm/CambazogluZCCLZD10 fatcat:s52wewestvde5j4igfc3x66mwu