- F. Radlinski, M. Kurup, T. Joachims, How Does Clickthrough Data Reflect Retrieval Quality?, Proceedings of the ACM Conference on Information and Knowledge Management (CIKM), ACM, 2008.
- F. Radlinski, R. Kleinberg, T. Joachims, Learning Diverse Rankings with Multi-Armed Bandits, Proceedings of the International Conference on Machine Learning (ICML), 2008. An Earlier version also appeared at a NIPS 2007 workshop. [PDF]
- F. Radlinski, A. Broder, P. Ciccolo, E. Gabrilovich, V. Josifovski, L. Riedel, Optimizing Relevance and Revenue in Ad Search: A Query Substitution Approach, Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), ACM, 2008. [PDF]
- F. Radlinski, T. Joachims, Active Exploration for Learning Rankings from Clickthrough Data, Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2007. Also presented at NESCAI '07. [PDF] [Slides]
- T. Joachims, F. Radlinski, Search Engines that Learn from Implicit Feedback, Computer, vol. 40 (8), pp. 34-40, August 2007. [Online]
- Y. Yue, T. Finley, F. Radlinski, T. Joachims, A Support Vector Method for Optimizing Average Precision, Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), ACM, 2007. [PDF] [Code]
- S. Pohl, F. Radlinski and T. Joachims, Recommending Related Papers Based on Digital Library Access Records, Short paper, Proceedings of Joint Conference on Digital Libraries (JCDL), 2007. [PDF]
- F. Radlinski, Addressing Malicious Noise in Clickthrough Data, Learning to Rank for Information Retrieval Workshop at SIGIR 2007. [PDF]
- T. Joachims, L. Granka, B. Pan, H. Hembrooke, F. Radlinski, G. Gay, Evaluating the Accuracy of Implicit Feedback from Clicks and Query Reformulations in Web Search, ACM Transactions on Information Systems (TOIS), vol. 25 (2), April 2007. [PDF]
- F. Radlinski and S. Dumais, Improving Personalized Web Search using Result Diversification, Poster Abstract, Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), ACM, 2006. [PDF]
- F. Radlinski and T. Joachims, Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs, Proceedings of the 21st National Conference on Artificial Intelligence (AAAI), 2006. Also presented as poster at NESCAI '06. [PDF]
- F. Radlinski and T. Joachims, Query Chains: Learning to Rank from Implicit Feedback, Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2005. Winner of best student paper award. [PDF] [Code]
- F. Radlinski and T. Joachims, Evaluating the Robustness of Learning from Implicit Feedback, ICML Workshop on Learning In Web Search, 2005. [PDF] [Code]
- E. Loken, F. Radlinski, V. H. Crespi and J. Millet, "New SAT Is to Old SAT as ...," APS Observer 18, pp. 15-16 (2005). [Online]
- E. Loken, F. Radlinski, V. Crespi, L. Cushing, J. Millet, Online study behavior of 100,000 students studying for the SAT, ACT and GRE. Journal of Educational Computing Research, 30, pp. 255-262 (2004). [Online]
I recently gave a tutorial about Learning to Rank at NESCAI '08 jointly with Yisong Yue.