{"id":485253,"date":"2018-05-08T19:43:14","date_gmt":"2018-05-09T02:43:14","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=485253"},"modified":"2018-10-16T22:26:22","modified_gmt":"2018-10-17T05:26:22","slug":"measuring-utility-search-engine-result-pages-information-foraging-based-measure","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/measuring-utility-search-engine-result-pages-information-foraging-based-measure\/","title":{"rendered":"Measuring the Utility of Search Engine Result Pages: An Information Foraging Based Measure"},"content":{"rendered":"

Web Search Engine Result Pages (SERPs) are complex responses to queries, containing many heterogeneous result elements (web results, advertisements, and specialized “answers”) positioned in a variety of layouts. This poses numerous challenges when trying to measure the quality of a SERP because standard measures were designed for homogeneous ranked lists.
\nIn this paper, we aim to measure the utility and cost of SERPs. To ground this work we adopt the C<\/strong>\/W<\/strong>\/L<\/strong> framework which enables a direct comparison between different measures in the same units of measurement, i.e. expected (total) utility and cost. Within this framework, we propose a new measure based on information foraging theory<\/em>, which can account for the heterogeneity of elements, through different costs, and which naturally motivates the development of a user stopping model that adapts behavior depending on the rate of gain. This directly connects models of how people search<\/em> with how we measure search<\/em>, providing a number of new dimensions in which to investigate and evaluate user behaviour and performance.
\nWe perform an analysis over 1000 popular queries issued to a major search engine, and report the aggregate utility experienced by users over time. Then in a comparison against common measures, we show that the proposed foraging based measure provides a more accurate reflection of the utility and of observed behaviours (stopping rank and time spent).<\/p>\n","protected":false},"excerpt":{"rendered":"

Web Search Engine Result Pages (SERPs) are complex responses to queries, containing many heterogeneous result elements (web results, advertisements, and specialized “answers”) positioned in a variety of layouts. This poses numerous challenges when trying to measure the quality of a SERP because standard measures were designed for homogeneous ranked lists. In this paper, we aim […]<\/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":"ACM","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval 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