Click Models for Web SearchMorgan & Claypool Publishers, 1 jul 2015 - 115 pagina's 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. |
Overige edities - Alles bekijken
Click Models for Web Search Aleksandr Chuklin,Ilya Markov,Maarten de Rijke Gedeeltelijke weergave - 2022 |
Click Models for Web Search Aleksandr Chuklin,Ilya Markov,Maarten de Rijke Geen voorbeeld beschikbaar - 2015 |
Click Models for Web Search Aleksandr Chuklin,Ilya Markov,Maarten de Rijke Geen voorbeeld beschikbaar - 2015 |
Veelvoorkomende woorden en zinsdelen
ACM Press aggregated search Aleksandr Chuklin algorithm applied attractiveness probability basic click models Bayesian network C H A P T E R Chapelle and Zhang Chapter CIKM click log click probability click-through rate clicked document compute conditional perplexity Cºr corresponding Craswell datasets DBN model DCTR depends discuss document at rank Dupret and Piwowarski EM algorithm evaluation metrics examination probability Ilya Markov Information Retrieval last-clicked likelihood log-likelihood Maarten de Rijke machine learning model Section mouse movements observed outperforms overfitting P.Au P.Cr P.Cu P.Er P.Er D P.Sr predicting clicks previous clicks probabilistic graphical models query q query sessions query-document pair query-document uq random variables RCTR relevance labels s2Suq satisfaction probability SDBN search engine search engine result Section 3.5 SERP SIGIR simulate user sponsored search update rule user behavior user clicks user model vertical Wang WSCD WSDM Yandex