{"id":162662,"date":"2019-01-17T09:53:46","date_gmt":"2019-01-17T17:53:46","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/anticipatory-search-using-context-to-initiate-search\/"},"modified":"2019-01-17T09:53:46","modified_gmt":"2019-01-17T17:53:46","slug":"anticipatory-search-using-context-to-initiate-search","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/anticipatory-search-using-context-to-initiate-search\/","title":{"rendered":"Anticipatory Search: Using Context to Initiate Search"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Identifying content for which a user may search has a variety of applications, including ranking and recommendation. In this post-er, we examine how pre-search context can be used to predict content that the user will seek before they have even specified a search query. We call this anticipatory search. Using a log-based approach, we compare different methods for predicting the con-tent to be searched using different attributes of the pre-query con-text and behavioral signals from previous visitors to the most-recent browse URL. Each method covers different cases and shows promise for query-free anticipatory search on the Web.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Identifying content for which a user may search has a variety of applications, including ranking and recommendation. In this post-er, we examine how pre-search context can be used to predict content that the user will seek before they have even specified a search query. We call this anticipatory search. Using a log-based approach, we compare [&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":[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-162662","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"Proceedings of the 35th Annual ACM SIGIR Conference (SIGIR 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