New Perspectives on Search Click Modeling | Personalized and Whole-page Click Models

Si Shen

ISBN 10: 3659183997 / ISBN 13: 9783659183997

Published by LAP LAMBERT Academic Publishing, 2012

About this Item

New Perspectives on Search Click Modeling | Personalized and Whole-page Click Models | Si Shen | Taschenbuch | 76 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659183997 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 106326156

Bibliographic Details

• Title: New Perspectives on Search Click Modeling | Personalized and Whole-page Click Models
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2012
• Language: English
• ISBN 10: 3659183997
• ISBN 13: 9783659183997
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 131 grams
• Seller catalogs:
  • Bücher

About this title

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.

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.