Synopsis
Click modeling aims to interpret the users’ search click data in order to predict their clicking behavior. In this book, we propose two directions of extending existing click model works: (1) expanding query-document relevance score with a user dimension, hence personalized click models capturing user intrinsic preferences by matrix and tensor factorization; and (2) using previous click models as a micro layer for each click in a macro click chain, which includes search click logs for every clickable block on the whole search result page. Either one of our perspectives on search click modeling produces a general framework that could incorporate existing click models and remains valid for possible future developments on position bias depiction. We verify both models through extensive experiments using large-scale data collected from a real search engine, and their improvements over current models are significant. In addition, our models are very capable of handling challenging problems in the literature, including prediction on rare queries and ads click interpretation, which may offer inspirations for future research.
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About the Author
Si Shen was an MPhil student in Computer Science and Engineering, supervised by Prof. Qiang Yang at the Hong Kong University of Science and Technology (HKUST). Prior to this, she obtained the degree of Bachelor of Science in Mathematics (Statistics) at HKUST. She was born in Beijing, China.
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New Perspectives on Search Click Modeling
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Click modeling aims to interpret the users search click data in order to predict their clicking behavior. In this book, we propose two directions of extending existing click model works: (1) expanding query-document relevance score with a user dimension, hence personalized click models capturing user intrinsic preferences by matrix and tensor factorization; and (2) using previous click models as a micro layer for each click in a macro click chain, which includes search click logs for every clickable block on the whole search result page. Either one of our perspectives on search click modeling produces a general framework that could incorporate existing click models and remains valid for possible future developments on position bias depiction. We verify both models through extensive experiments using large-scale data collected from a real search engine, and their improvements over current models are significant. In addition, our models are very capable of handling challenging problems in the literature, including prediction on rare queries and ads click interpretation, which may offer inspirations for future research. 76 pp. Englisch. Seller Inventory # 9783659183997
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Shen SiSi Shen was an MPhil student in Computer Science and Engineering, supervised by Prof. Qiang Yang at the Hong Kong University of Science and Technology (HKUST). Prior to this, she obtained the degree of Bachelor of Science in M. Seller Inventory # 5137783
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New Perspectives on Search Click Modeling
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LAP LAMBERT Academic Publishing Aug 2012, 2012
ISBN 10: 3659183997
ISBN 13: 9783659183997
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Click modeling aims to interpret the users' search click data in order to predict their clicking behavior. In this book, we propose two directions of extending existing click model works: (1) expanding query-document relevance score with a user dimension, hence personalized click models capturing user intrinsic preferences by matrix and tensor factorization; and (2) using previous click models as a micro layer for each click in a macro click chain, which includes search click logs for every clickable block on the whole search result page. Either one of our perspectives on search click modeling produces a general framework that could incorporate existing click models and remains valid for possible future developments on position bias depiction. We verify both models through extensive experiments using large-scale data collected from a real search engine, and their improvements over current models are significant. In addition, our models are very capable of handling challenging problems in the literature, including prediction on rare queries and ads click interpretation, which may offer inspirations for future research.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 76 pp. Englisch. Seller Inventory # 9783659183997
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