Implicit feedback algorithms utilize interaction between
\nsearchers and search systems to learn more about users\u2019
\nneeds and interests than expressed in query statements
\nalone. This additional information can be used to formulate
\nimproved queries or directly improve retrieval performance.
\nIn this paper we present a geometric framework
\nthat utilizes multiple sources of evidence present in this interaction
\ncontext (e.g., display time, document retention)
\nto develop enhanced implicit feedback models personalized
\nfor each user and tailored for each search task. We use rich
\ninteraction logs (and associated metadata such as relevance
\njudgments), gathered during a longitudinal user study, as
\nrelevance stimuli to compare an implicit feedback algorithm
\ndeveloped using the framework with alternative algorithms.
\nOur findings demonstrate both the effectiveness of our proposed
\nalgorithm and the potential value of incorporating
\nmultiple sources of interaction evidence when developing implicit
\nfeedback algorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"
Implicit feedback algorithms utilize interaction between searchers and search systems to learn more about users\u2019 needs and interests than expressed in query statements alone. This additional information can be used to formulate improved queries or directly improve retrieval performance. In this paper we present a geometric framework that utilizes multiple sources of evidence present in […]<\/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":[243138],"msr-publication-type":[193716],"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-150857","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-quantum","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Proceedings of the 16th Annual ACM CIKM Conference on Information and Knowledge Management (CIKM)","msr_affiliation":"","msr_published_date":"2007-01-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","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":"208851","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"meluccicikm2007.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/meluccicikm2007.pdf","id":208851,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":208851,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/meluccicikm2007.pdf"}],"msr-author-ordering":[{"type":"text","value":"M. 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