{"id":160490,"date":"2011-02-09T00:00:00","date_gmt":"2011-02-09T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/understanding-and-predicting-personal-navigation\/"},"modified":"2018-10-16T20:19:32","modified_gmt":"2018-10-17T03:19:32","slug":"understanding-and-predicting-personal-navigation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/understanding-and-predicting-personal-navigation\/","title":{"rendered":"Understanding and Predicting Personal Navigation"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper presents an algorithm that predicts with very high accuracy which Web search result a user will click for one sixth of all Web queries. Prediction is done via a straightforward form of personalization that takes advantage of the fact that people often use search engines to re-find previously viewed resources. In our approach, an individual\u2019s past navigational behavior is identified via query log analysis and used to forecast identical future navigational behavior by the same individual. We compare the potential value of personal navigation with general navigation identified using aggregate user behavior. Although consistent navigational behavior across users can be useful for identifying a subset of navigational queries, different people often use the same queries to navigate to different resources. This is true even for queries comprised of unambiguous company names or URLs and typically thought of as navigational. We build an understanding of what personal navigation looks like, and identify ways to improve its coverage and accuracy by taking advantage of people\u2019s consistency over time and across groups of individuals.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents an algorithm that predicts with very high accuracy which Web search result a user will click for one sixth of all Web queries. Prediction is done via a straightforward form of personalization that takes advantage of the fact that people often use search engines to re-find previously viewed resources. In our approach, [&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":"","msr-author-ordering":null,"msr_publishername":"Association for Computing Machinery, Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proc. WSDM 2011","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. 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