{"id":159414,"date":"2009-12-01T00:00:00","date_gmt":"2009-12-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/novel-tools-to-streamline-the-conference-review-process-experiences-from-sigkdd09\/"},"modified":"2018-10-16T21:27:15","modified_gmt":"2018-10-17T04:27:15","slug":"novel-tools-to-streamline-the-conference-review-process-experiences-from-sigkdd09","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/novel-tools-to-streamline-the-conference-review-process-experiences-from-sigkdd09\/","title":{"rendered":"Novel Tools To Streamline the Conference Review Process: Experiences from SIGKDD&#8217;09"},"content":{"rendered":"<div class=\"asset-content\">\n<p>The SIGKDD&#8217;09 Research Track received 537 paper submissions, which were reviewed by a Program Committee of 199 members, and a Senior Program Committee of 22 members. We used techniques from artificial intelligence and data mining to streamline and support this complicated process at three crucial stages: bidding by PC members on papers, assigning papers to reviewers, and calibrating scores obtained from the reviews. In this paper we report on the approaches taken, evaluate how well they worked, and describe some further work done after the conference.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The SIGKDD&#8217;09 Research Track received 537 paper submissions, which were reviewed by a Program Committee of 199 members, and a Senior Program Committee of 22 members. We used techniques from artificial intelligence and data mining to streamline and support this complicated process at three crucial stages: bidding by PC members on papers, assigning papers to [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13555],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-159414","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"Association for Computing Machinery, Inc.","msr_edition":"","msr_affiliation":"","msr_published_date":"2009-12-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"SIGKDD Explorations","msr_volume":"11","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"222208","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"ReviewerCalibration.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2009\/12\/ReviewerCalibration.pdf","id":222208,"label_id":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Peter A. Flach","user_id":0,"rest_url":false},{"type":"text","value":"Sebastian Spiegler","user_id":0,"rest_url":false},{"type":"text","value":"Bruno Golenia","user_id":0,"rest_url":false},{"type":"text","value":"Simon Price","user_id":0,"rest_url":false},{"type":"user_nicename","value":"joguiver","user_id":32363,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=joguiver"},{"type":"user_nicename","value":"rherb","user_id":33390,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=rherb"},{"type":"user_nicename","value":"thoreg","user_id":34034,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=thoreg"},{"type":"text","value":"Mohammed J. Zaki","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[169873],"publication":[],"video":[],"download":[],"msr_publication_type":"article","related_content":{"projects":[{"ID":169873,"post_title":"TrueSkill\u2122 Ranking System","post_name":"trueskill-ranking-system","post_type":"msr-project","post_date":"2005-11-18 07:02:09","post_modified":"2024-06-20 05:32:08","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/trueskill-ranking-system\/","post_excerpt":"The TrueSkill ranking system is a skill based ranking system for\u00a0Xbox Live developed at Microsoft Research. The purpose of a ranking system is to both identify and track the skills of gamers in a game (mode) in order to be able to match them into competitive matches. TrueSkill has been used to rank and match players in many different games, from Halo 3 to Forza Motorsport 7. 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