{"id":162539,"date":"2009-01-01T00:00:00","date_gmt":"2009-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/fpga-acceleration-of-rankboost-in-web-search-engines\/"},"modified":"2018-10-16T19:58:52","modified_gmt":"2018-10-17T02:58:52","slug":"fpga-acceleration-of-rankboost-in-web-search-engines","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/fpga-acceleration-of-rankboost-in-web-search-engines\/","title":{"rendered":"FPGA Acceleration of RankBoost in Web Search Engines"},"content":{"rendered":"

Search relevance is a key measurement for the usefulness of search engines. Shift of search relevance among search engines can easily change a search company’s market cap by tens of billions of dollars. With the ever-increasing scale of the Web, machine learning technologies have become important tools to improve search relevance ranking. RankBoost is a promising algorithm in this area, but it is not widely used due to its long training time. To reduce the computation time for RankBoost, we designed a FPGA-based accelerator system and its upgraded version. The accelerator, plugged into a commodity PC, increased the training speed on MSN search engine data up to 1800x compared to the original software implementation on a server. The proposed accelerator has been successfully used by researchers in the search relevance ranking.<\/p>\n","protected":false},"excerpt":{"rendered":"

Search relevance is a key measurement for the usefulness of search engines. Shift of search relevance among search engines can easily change a search company’s market cap by tens of billions of dollars. With the ever-increasing scale of the Web, machine learning technologies have become important tools to improve search relevance ranking. RankBoost is a […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13552,13555,13547],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-162539","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-hardware-devices","msr-research-area-search-information-retrieval","msr-research-area-systems-and-networking","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"ACM Trans. Reconfigurable Technol. 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