Publication | Closed Access
Accurately interpreting clickthrough data as implicit feedback
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Citations
22
References
2005
Year
Unknown Venue
EngineeringInteractive SearchClickthrough DataInteractive Machine LearningInformation RetrievalData ScienceBiasManagementRelevance FeedbackDecision TheoryCognitive ScienceAbsolute Relevance JudgmentsDecision ProcessUser ExperiencePersonalized SearchUser FeedbackQuery AnalysisWww SearchInteractive MarketingHuman-computer InteractionInteractive Information Retrieval
This 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.
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