Eye-tracking analysis of user behavior in WWW search
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Presentation bias is significant in determining user preference for search results—A user study
Journal of the American Society for Information Science and Technology
Integration of news content into web results
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Click chain model in web search
Proceedings of the 18th international conference on World wide web
Sources of evidence for vertical selection
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 19th international conference on World wide web
Vertical selection in the presence of unlabeled verticals
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
A methodology for evaluating aggregated search results
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
User-click modeling for understanding and predicting search-behavior
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to aggregate vertical results into web search results
Proceedings of the 20th ACM international conference on Information and knowledge management
A noise-aware click model for web search
Proceedings of the fifth ACM international conference on Web search and data mining
Beyond ten blue links: enabling user click modeling in federated web search
Proceedings of the fifth ACM international conference on Web search and data mining
Mixture model with multiple centralized retrieval algorithms for result merging in federated search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Evaluating reward and risk for vertical selection
Proceedings of the 21st ACM international conference on Information and knowledge management
Incorporating user preferences into click models
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Robust models of mouse movement on dynamic web search results pages
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In modern search engines, an increasing number of search result pages (SERPs) are federated from multiple specialized search engines (called verticals, such as Image or Video). As an effective approach to interpret users' click-through behavior as feedback information, most click models were designed to reduce the position bias and improve ranking performance of ordinary search results, which have homogeneous appearances. However, when vertical results are combined with ordinary ones, significant differences in presentation may lead to user behavior biases and thus failure of state-of-the-art click models. With the help of a popular commercial search engine in China, we collected a large scale log data set which contains behavior information on both vertical and ordinary results. We also performed eye-tracking analysis to study user's real-world examining behavior. According these analysis, we found that different result appearances may cause different behavior biases both for vertical results (local effect) and for the whole result lists (global effect). These biases include: examine bias for vertical results (especially those with multimedia components), trust bias for result lists with vertical results, and a higher probability of result revisitation for vertical results. Based on these findings, a novel click model considering these biases besides position bias was constructed to describe interaction with SERPs containing verticals. Experimental results show that the new Vertical-aware Click Model (VCM) is better at interpreting user click behavior on federated searches in terms of both log-likelihood and perplexity than existing models.