{"id":351284,"date":"2017-01-12T11:31:40","date_gmt":"2017-01-12T19:31:40","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=351284"},"modified":"2018-10-16T19:56:32","modified_gmt":"2018-10-17T02:56:32","slug":"implicit-feedback-inferring-user-preference-bibliography","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/implicit-feedback-inferring-user-preference-bibliography\/","title":{"rendered":"Implicit Feedback for Inferring User Preference: A Bibliography"},"content":{"rendered":"<p>Relevance feedback has a history in information retrieval that dates back well over thirty years. Relevance feedback is typically used for query expansion during short-term modeling of a user&#8217;s immediate information need and for user profiling during long-term modeling of a user&#8217;s persistent interested and preferences.<\/p>\n<p>In this paper we consider the use of implicit feedback techniques for query expansion and user profiling in information retrieval tasks.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Relevance feedback has a history in information retrieval that dates back well over thirty years. Relevance feedback is typically used for query expansion during short-term modeling of a user&#8217;s immediate information need and for user profiling during long-term modeling of a user&#8217;s persistent interested and preferences. In this paper we consider the use of implicit [&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":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"2","msr_journal":"SIGIR 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