{"id":2199,"date":"2019-02-23T10:15:56","date_gmt":"2019-02-23T10:15:56","guid":{"rendered":"https:\/\/staff.fnwi.uva.nl\/m.derijke\/?p=2199"},"modified":"2019-02-23T10:15:56","modified_gmt":"2019-02-23T10:15:56","slug":"www-2019-paper-on-visual-learning-to-rank-online","status":"publish","type":"post","link":"https:\/\/staff.fnwi.uva.nl\/m.derijke\/www-2019-paper-on-visual-learning-to-rank-online\/","title":{"rendered":"WWW 2019 paper on visual learning to rank online"},"content":{"rendered":"\n<p><em>ViTOR: Learning to Rank Webpages Based on Visual Features<\/em> by Bram van den Akker, Ilya Markov, and\u00a0Maarten de Rijke is available online now <a href=\"https:\/\/staff.fnwi.uva.nl\/m.derijke\/wp-content\/papercite-data\/pdf\/van-den-akker-2019-vitor.pdf\">at this location<\/a>.<\/p>\n\n\n\n<p>The visual appearance of a webpage carries valuable information about the page\u2019s quality and can be used to improve the performance of learning to rank (LTR). We introduce the\u00a0Visual learning TO Rank\u00a0(ViTOR) model that integrates state-of-the-art visual features extraction methods: (i) transfer learning from a pre-trained image classification model, and (ii) synthetic saliency heat maps generated from webpage snapshots. Since there is currently no public dataset for the task of LTR with visual features, we also introduce and release the ViTOR dataset, containing visually rich and diverse webpages. The ViTOR dataset consists of visual snapshots, non-visual features and relevance judgments for ClueWeb12 webpages and TREC Web Track queries. We experiment with the proposed ViTOR model on the ViTOR dataset and show that it significantly improves the performance of LTR with visual features.<\/p>\n\n\n\n<p>The paper will be presented at The Web Conference 2019.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>ViTOR: Learning to Rank Webpages Based on Visual Features by Bram van den Akker, Ilya Markov, and\u00a0Maarten de Rijke is available online now at this location. The visual appearance of a webpage carries valuable information about the page\u2019s quality and can be used to improve the performance of learning to rank (LTR). We introduce the\u00a0Visual&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/staff.fnwi.uva.nl\/m.derijke\/wp-json\/wp\/v2\/posts\/2199"}],"collection":[{"href":"https:\/\/staff.fnwi.uva.nl\/m.derijke\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/staff.fnwi.uva.nl\/m.derijke\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/staff.fnwi.uva.nl\/m.derijke\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/staff.fnwi.uva.nl\/m.derijke\/wp-json\/wp\/v2\/comments?post=2199"}],"version-history":[{"count":1,"href":"https:\/\/staff.fnwi.uva.nl\/m.derijke\/wp-json\/wp\/v2\/posts\/2199\/revisions"}],"predecessor-version":[{"id":2200,"href":"https:\/\/staff.fnwi.uva.nl\/m.derijke\/wp-json\/wp\/v2\/posts\/2199\/revisions\/2200"}],"wp:attachment":[{"href":"https:\/\/staff.fnwi.uva.nl\/m.derijke\/wp-json\/wp\/v2\/media?parent=2199"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/staff.fnwi.uva.nl\/m.derijke\/wp-json\/wp\/v2\/categories?post=2199"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/staff.fnwi.uva.nl\/m.derijke\/wp-json\/wp\/v2\/tags?post=2199"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}