A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Towards recency ranking in web search
Proceedings of the third ACM international conference on Web search and data mining
Incorporating post-click behaviors into a click model
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Online learning for recency search ranking using real-time user feedback
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Understanding temporal query dynamics
Proceedings of the fourth ACM international conference on Web search and data mining
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User behavior on search results pages provides a clue about the query intent and the relevance of documents. To incorporate this information into search rankings, a variety of click modeling techniques have been proposed so far and now they are widely used in commercial search engines. For time-sensitive queries, however, applying click models can degrade the search relevance because the best document in the past may not be the current best answer. To address this problem, it is required to detect a time point, a turning point, where the search intent for a given query changes and to reflect it in click models. In this work, we devised a method to detect the turning point of a query from its search volume history. The proposed click model is designed to take only user behavior observed after the turning points. We applied our model in a commercial search engine and evaluated its relevance.