Info Sci professors Thorsten Joachims and Geri Gay were among the authors of a 2005 paper that recently received ACM SIGIR's (Special Interest Group on Information Retrieval) Test of Time Award.
"Accurately Interpreting Clickthrough Data as Implicit Feedback" was one of the first papers to rigorously explore the behavioral biases in implicit feedback from user behavior, informing how to properly use machine-learning methods in order to learn from such data. Joachims, Laura Granka, Bing Pan, Helene Hembrooke, and Gay were authors of this breakthrough research.
In particular, the paper combined insights and experimental methodology from the behavioral social sciences with the theory underlying machine learning algorithms. The award recognizes the work's impact on information retrieval research, as well as on how search engines and other online systems use machine learning today. The winning paper is selected from the set of full papers presented at the main SIGIR conference 10-12 years before.