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
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
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
ACM SIGIR Forum
Efficient multiple-click models in web search
Proceedings of the Second ACM International Conference on Web Search and Data Mining
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
BBM: bayesian browsing model from petabyte-scale data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Sources of evidence for vertical selection
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Adaptation of offline vertical selection predictions in the presence of user feedback
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine
Proceedings of the third ACM international conference on Web search and data mining
A novel click model and its applications to online advertising
Proceedings of the third ACM international conference on Web search and data mining
Temporal click model for sponsored search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Incorporating post-click behaviors into a click model
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Vertical selection in the presence of unlabeled verticals
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
User browsing models: relevance versus examination
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Factors affecting click-through behavior in aggregated search interfaces
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Proceedings of the fourth ACM international conference on Web search and data mining
Characterizing search intent diversity into click models
Proceedings of the 20th international conference on World wide web
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
Evaluating aggregated search pages
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Using intent information to model user behavior in diversified search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Incorporating vertical results into search click models
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Click model-based information retrieval metrics
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
A unified search federation system based on online user feedback
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluating aggregated search using interleaving
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Lerot: an online learning to rank framework
Proceedings of the 2013 workshop on Living labs for information retrieval evaluation
Estimating ad group performance in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
Hi-index | 0.00 |
Click models have been positioned as an effective approach to interpret user click behavior in search engines. Existing click models mostly focus on traditional Web search that considers only ten homogeneous Web HTML documents that appear on the first search-result page. However, in modern commercial search engines, more and more Web search results are federated from multiple sources and contain non-HTML results returned by other heterogeneous vertical engines, such as video or image search engines. In this paper, we study user click behavior in federated search. We observed that user click behavior in federated search is highly different from that in traditional Web search, making it difficult to interpret using existing click models. In response, we propose a novel federated click model (FCM) to interpret user click behavior in federated search. In particular, we take into considerations two new biases in FCM. The first comes from the observation that users tend to be attracted by vertical results and their visual attention on them may increase the examination probability of other nearby web results. The other illustrates that user click behavior on vertical results may lead to more clues of search relevance due to their presentation style in federated search. With these biases and an effective model to correct them, FCM is more accurate in characterizing user click behavior in federated search. Our extensive experimental results show that FCM can outperform other click models in interpreting user click behavior in federated search and achieve significant improvements in terms of both perplexity and log-likelihood.