Click Models for Web Search
mai 2022 · Springer Nature
E-book
99
Pages
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À propos de cet e-book
With the rapid growth of web search in recent years the problem of modeling its users has started to attract more and more attention of the information retrieval community. This has several motivations. By building a model of user behavior we are essentially developing a better understanding of a user, which ultimately helps us to deliver a better search experience. A model of user behavior can also be used as a predictive device for non-observed items such as document relevance, which makes it useful for improving search result ranking. Finally, in many situations experimenting with real users is just infeasible and hence user simulations based on accurate models play an essential role in understanding the implications of algorithmic changes to search engine results or presentation changes to the search engine result page. In this survey we summarize advances in modeling user click behavior on a web search engine result page. We present simple click models as well as more complex models aimed at capturing non-trivial user behavior patterns on modern search engine result pages. We discuss how these models compare to each other, what challenges they have, and what ways there are to address these challenges. We also study the problem of evaluating click models and discuss the main applications of click models.
À propos de l'auteur
Aleksandr Chuklin is a Software Engineer working on search problems at Google Switzerland. Apart from his projects at Google he is also working with the Information and Language Processing Systems group at the University of Amsterdam on a number of research topics. He received his MSc degree from the Moscow Institute of Physics and Technology in 2012. His main research interests are modeling and understanding user behavior on a search engine result page. Aleksandr has a number of publications on click models and their applications at SIGIR, CIKM, ECIR. He is also PC member of the CIKM and WSDM conferences. Ilya Markov is a postdoctoral researcher and SNF fellow at the University of Amsterdam. His research agenda builds around information retrieval methods for heterogeneous search environments. Ilya has experience in federated search, user behavior analysis, click models and effectiveness metrics. He is a PC member of leading IR conferences, such as SIGIR, WWW and ECIR, a PC chair of the RuSSIR 2015 summer school and a co-organizer of the IMine-2 task at NTCIR-12. Ilya is currently teaching an MSc course on web search and has previously taught information retrieval courses at the BSc and MSc levels and given tutorials at conferences and summer schools in IR (ECIR, RuSSIR). Maarten de Rijke is Professor of Computer Science at the University of Amsterdam. He leads a large team of researchers in information retrieval. His recent research focus is on (online) ranking and evaluation and on semantic search. Maarten has authored over 650 papers, many of which are core to this tutorial, and is Editor-in-Chief of ACM Transactions on Information Systems. He has supervised or is supervising over 40 Ph.D. students. He has taught at the primary school, high school, BSc, MSc and Ph.D. levels, as well as for general audiences, with recent tutorials at ECIR, ESSIR and SIGIR.
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