Search engines record a large amount of metadata each time a user issues a query. While efficiently mining this data can be challenging, the results can be useful in multiple ways, including monitoring search engine performance, improving search relevance, prioritizing research, and optimizing day-to-day operations. In this poster, we describe an approach for mining query log data for actionable insights \u2013 specific query segments (sets of queries) that require attention, and actions that need to be taken to improve the segments. Starting with a set of important metrics, we identify query segments that are \u201cinteresting\u201d with respect to these metrics using a distributed frequent itemset mining algorithm.<\/p>\n","protected":false},"excerpt":{"rendered":"
Search engines record a large amount of metadata each time a user issues a query. While efficiently mining this data can be challenging, the results can be useful in multiple ways, including monitoring search engine performance, improving search relevance, prioritizing research, and optimizing day-to-day operations. In this poster, we describe an approach for mining query […]<\/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":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"21st International World Wide Web Conference (WWW 2012), Pages: 489-490 Lyon, France","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"489-490","msr_page_range_start":"489","msr_page_range_end":"490","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"21st International World Wide Web Conference (WWW 2012), Pages: 489-490 Lyon, 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