]> BibSonomy publications for/author/Joachims/clickthrough New York, NY, USASIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval154--161Accurately interpreting clickthrough data as implicit feedback2005clickthrough feedback toread 2008-09-22 10:56:57.0This paper examines the reliability of implicit feedback generated from clickthrough data in WWW search. Analyzing the users' decision process using eyetracking and comparing implicit feedback against manual relevance judgments, we conclude that clicks are informative but biased. While this makes the interpretation of clicks as absolute relevance judgments difficult, we show that relative preferences derived from clicks are reasonably accurate on average. ACM SIGKDD International Conference On Knowledge Discovery and Data Mining (KDD)Active Exploration for Learning Rankings from Clickthrough Data2007TREC clickthrough learning rankings 2007-10-17 14:35:49.0 ACM SIGKDD International Conference On Knowledge Discovery and Data Mining (KDD)239--248Query Chains: Learning to Rank from Implicit Feedback2005clickthrough learning query-chains ranking svm 2007-10-17 14:26:23.0 \cite{KDD2002}133--142Optimizing Search Engines using Clickthrough Data2002clickthrough data engines optimizing search 2005-12-20 20:21:42.0