Early exit optimizations for additive machine learned ranking systems

B. Barla Cambazoglu, Hugo Zaragoza, Olivier Chapelle, Jiang Chen, Ciya Liao, Zhaohui Zheng, Jon Degenhardt
2010 Proceedings of the third ACM international conference on Web search and data mining - WSDM '10  
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 learning
more » ... stems. The strategies are evaluated over a state-of-the-art machine learning system and a large, real-life query log, obtained from Yahoo!. By the proposed strategies, we are able to speedup the score computations by more than four times with almost no loss in result quality.
doi:10.1145/1718487.1718538 dblp:conf/wsdm/CambazogluZCCLZD10 fatcat:s52wewestvde5j4igfc3x66mwu