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Web"],"published-print":{"date-parts":[[2018,8,31]]},"abstract":"<jats:p>\n            Modern search engines aggregate results from different\n            <jats:italic>verticals<\/jats:italic>\n            : webpages, news, images, video, shopping, knowledge cards, local maps, and so on. Unlike \u201cten blue links,\u201d these search results are heterogeneous in nature and not even arranged in a list on the page. This revolution directly challenges the conventional \u201cranked list\u201d formulation in ad hoc search. Therefore, finding proper\n            <jats:italic>presentation<\/jats:italic>\n            for a gallery of heterogeneous results is critical for modern search engines.\n          <\/jats:p>\n          <jats:p>\n            We propose a novel framework that learns the optimal\n            <jats:italic>page presentation<\/jats:italic>\n            to render heterogeneous results onto search result page (SERP). Page presentation is broadly defined as the strategy to present a set of items on SERP, much more expressive than a ranked list. It can specify item positions, image sizes, text fonts, and any other styles as long as variations are within business and design constraints. The learned presentation is content aware, i.e., tailored to specific queries and returned results. Simulation experiments show that the framework automatically learns eye-catchy presentations for relevant results. Experiments on real data show that simple instantiations of the framework already outperform leading algorithm in federated search result presentation. It means the framework can\n            <jats:italic>learn<\/jats:italic>\n            its own result presentation strategy purely from data, without even knowing the \u201cprobability ranking principle.\u201d\n          <\/jats:p>","DOI":"10.1145\/3204461","type":"journal-article","created":{"date-parts":[[2018,7,17]],"date-time":"2018-07-17T12:45:12Z","timestamp":1531831512000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Optimizing Whole-Page Presentation for Web Search"],"prefix":"10.1145","volume":"12","author":[{"given":"Yue","family":"Wang","sequence":"first","affiliation":[{"name":"University of Michigan, Ann Arbor, MI"}]},{"given":"Dawei","family":"Yin","sequence":"additional","affiliation":[{"name":"Data Science Lab, JD.com, Beijing, China"}]},{"given":"Luo","family":"Jie","sequence":"additional","affiliation":[{"name":"Snap Inc., Venice, CA"}]},{"given":"Pengyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Georgia, USA"}]},{"given":"Makoto","family":"Yamada","sequence":"additional","affiliation":[{"name":"Kyoto University\/RIKEN AIP, Japan"}]},{"given":"Yi","family":"Chang","sequence":"additional","affiliation":[{"name":"Jilin University, China"}]},{"given":"Qiaozhu","family":"Mei","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, MI"}]}],"member":"320","published-online":{"date-parts":[[2018,7,17]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098155"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1148170.1148177"},{"key":"e_1_2_1_3_1","volume-title":"Relevance Ranking for Vertical Search Engines","author":"Arguello Jaime","unstructured":"Jaime Arguello and Fernando Diaz . 2013. Vertical selection and aggregation . In Relevance Ranking for Vertical Search Engines , Bo Long and Yi Chang (Eds.). Elsevier . Jaime Arguello and Fernando Diaz. 2013. Vertical selection and aggregation. In Relevance Ranking for Vertical Search Engines, Bo Long and Yi Chang (Eds.). Elsevier."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2063576.2063611"},{"key":"e_1_2_1_5_1","volume-title":"Advances in Information Retrieval","author":"Arguello Jaime","unstructured":"Jaime Arguello , Fernando Diaz , Jamie Callan , and Ben Carterette . 2011. A methodology for evaluating aggregated search results . In Advances in Information Retrieval . Springer , 141--152. Jaime Arguello, Fernando Diaz, Jamie Callan, and Ben Carterette. 2011. A methodology for evaluating aggregated search results. In Advances in Information Retrieval. 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