Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGIR Forum
Understanding user goals in web search
Proceedings of the 13th 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 dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
LETOR: A benchmark collection for research on learning to rank for information retrieval
Information Retrieval
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Ground truth labels are one of the most important parts in many test collections for information retrieval. Each label, depicting the relevance between a query-document pair, is usually judged by a human, and this process is time-consuming and labor-intensive. Automatically Generating labels from click-through data has attracted increasing attention. In this paper, we propose a Unified Click Model to predict the multi-level labels, which aims at comprehensively considering the advantages of the Position Models and Cascade Models. Experiments show that the proposed click model outperforms the existing click models in predicting the multi-level labels, and could replace the labels judged by humans for test collections.