{"id":161484,"date":"2011-01-01T00:00:00","date_gmt":"2011-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/detecting-seasonal-queries-by-time-series-analysis\/"},"modified":"2018-10-16T19:57:50","modified_gmt":"2018-10-17T02:57:50","slug":"detecting-seasonal-queries-by-time-series-analysis","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/detecting-seasonal-queries-by-time-series-analysis\/","title":{"rendered":"Detecting Seasonal Queries by Time-Series Analysis"},"content":{"rendered":"<div class=\"asset-content\">Seasonal events such as Halloween and Christmas repeat every year and initiate several temporal information needs. The impact of such events on users is often re\ufb02ected in search logs in form of seasonal spikes in the frequency of related queries (e.g. \u201challoween costumes\u201d, \u201cwhere is santa\u201d). Many seasonal queries such as\u201csigir conference\u201dmainly target fresh pages (e.g. sigir2011.org) that have less usage data such as clicks and anchor-text compared to older alternatives (e.g. sigir2009.org). Thus, it is important for search engines to correctly identify seasonal queries and make sure that their results are temporally reordered if necessary. In this poster, we focus on detecting seasonal queries using time-series analysis. We demonstrate that the seasonality of a query can be determined with high accuracy according to its historical frequency distribution.<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Seasonal events such as Halloween and Christmas repeat every year and initiate several temporal information needs. The impact of such events on users is often re\ufb02ected in search logs in form of seasonal spikes in the frequency of related queries (e.g. \u201challoween costumes\u201d, \u201cwhere is santa\u201d). Many seasonal queries such as\u201csigir conference\u201dmainly target fresh pages [&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":[13554],"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-161484","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"ACM","msr_edition":"Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information 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