{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T00:54:15Z","timestamp":1723683255468},"reference-count":43,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T00:00:00Z","timestamp":1569456000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["Grant No. 61622208, 61732008, 61532011"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key Research and Development Program of China","award":["2018YFC0831700"]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2019,10,31]]},"abstract":"A series of click models has been proposed to extract accurate and unbiased relevance feedback from valuable yet noisy click-through data in search logs. Previous works have shown that users search behavior in mobile and desktop scenarios are rather different in many aspects, therefore, the click models designed for desktop search may not be effective in the mobile context. To address this problem, we propose two novel click models for mobile search: (1) Mobile Click Model (MCM), which models click necessity bias and examination satisfaction bias; (2) Viewport Time Click Model (VTCM), which further extends MCM by utilizing the viewport time. Extensive experiments on large-scale real mobile search logs show that: (1) MCM and VTCM outperform existing models in predicting users\u2019 clicks and estimating result relevance; (2) MCM and VTCM can extract richer information, such as the click necessity of search results and the probability of user satisfaction, from mobile click logs; (3) By modeling the viewport time distributions of heterogeneous results, VTCM can bring a significant improvement over MCM in click prediction and relevance estimation tasks. Our proposed click models can help better understand user behavior patterns in mobile search and improve the ranking performance of mobile search engines.<\/jats:p>","DOI":"10.1145\/3360486","type":"journal-article","created":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T12:17:42Z","timestamp":1569500262000},"page":"1-34","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Constructing Click Model for Mobile Search with Viewport Time"],"prefix":"10.1145","volume":"37","author":[{"given":"Yukun","family":"Zheng","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Jiaxin","family":"Mao","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Yiqun","family":"Liu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Cheng","family":"Luo","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Shaoping","family":"Ma","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2019,9,26]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883033"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1645953.1646033"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526711"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124351"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2983323.2983829"},{"key":"e_1_2_1_6_1","first-page":"1","article-title":"Click models for web search. 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