{"id":215224,"date":"2014-11-01T00:00:00","date_gmt":"2014-11-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-crowd-of-your-own-crowdsourcing-for-on-demand-personalization\/"},"modified":"2018-10-16T21:43:58","modified_gmt":"2018-10-17T04:43:58","slug":"a-crowd-of-your-own-crowdsourcing-for-on-demand-personalization","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-crowd-of-your-own-crowdsourcing-for-on-demand-personalization\/","title":{"rendered":"A Crowd of Your Own: Crowdsourcing for On-Demand Personalization"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Personalization is a way for computers to support people\u2019s diverse interests and needs by providing content tailored to the individual. While strides have been made in algorithmic approaches to personalization, most require access to a significant amount of data. However, even when data is limited online crowds can be used to infer an individual\u2019s personal preferences. Aided by the diversity of tastes among online crowds and their ability to understand others, we show that crowdsourcing is an effective on-demand tool for personalization. Unlike typical crowdsourcing approaches that seek a ground truth, we present and evaluate two crowdsourcing approaches designed to capture personal preferences. The first, <em>taste-matching<\/em>, identifies workers with similar taste to the requester and uses their taste to infer the requester\u2019s taste. The second, <em>taste-grokking<\/em>, asks workers to explicitly predict the requester\u2019s taste based on training examples. These techniques are evaluated on two subjective tasks, personalized image recommendation and tailored textual summaries. Taste-matching and tastegrokking both show improvement over the use of generic workers, and have different benefits and drawbacks depending on the complexity of the task and the variability of the taste space.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Personalization is a way for computers to support people\u2019s diverse interests and needs by providing content tailored to the individual. While strides have been made in algorithmic approaches to personalization, most require access to a significant amount of data. However, even when data is limited online crowds can be used to infer an individual\u2019s personal [&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":[13556,13554,13555],"msr-publication-type":[193716],"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-215224","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-computer-interaction","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"AAAI - Association for the Advancement of Artificial Intelligence","msr_edition":"HCOMP 2014","msr_affiliation":"","msr_published_date":"2014-11-01","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":"Notable Paper","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":"215523","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"hcomp14.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/04\/hcomp14.pdf","id":215523,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":215523,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/04\/hcomp14.pdf"}],"msr-author-ordering":[{"type":"text","value":"Peter Organisciak","user_id":0,"rest_url":false},{"type":"user_nicename","value":"teevan","user_id":33975,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=teevan"},{"type":"user_nicename","value":"sdumais","user_id":33565,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=sdumais"},{"type":"text","value":"Robert C. 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