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Address: Microsoft Research Ltd 7 JJ Thomson Avenue Cambridge, CB3 0FB, UK
Email: filiprad [at] microsoft.com Phone: +44 1223 479 960 |
I am an applied researcher at Microsoft Research Cambridge. I am in the Machine Learning and Perception group, the Information Retrieval and Analysis group, and the Bing group.
My current research focuses on developing machine learning techniques for learning from, evaluating with, and optimizing to implicitly collected feedback from web users. In particular I am interested in applications to web search and online advertising, with a current focus on diversity in web search and web scale online learning algorithms.
Before joining Microsoft in mid 2008, I completed my PhD in Computer Science at Cornell University. I also have a personal homepage.
News
I'm an organizer of the workshop on Redundancy, Diversity, and Interdependent Document Relevance at SIGIR 2009.
2009
- Deepayan Chakrabarti, Ravi Kumar, Filip Radlinski, and Eli Upfal, Mortal Multi-Armed Bandits, in Advances in Neural Information Processing Systems (NIPS), 2009
2008
- Filip Radlinski, Madhu Kurup, and Thorsten Joachims, How Does Clickthrough Data Reflect Retrieval Quality?, in Proceedings of the ACM Conference on Information and Knowledge Management (CIKM), ACM, 2008
- Filip Radlinski, Robert Kleinberg, and Thorsten Joachims, Learning Diverse Rankings with Multi-Armed Bandits, in Proceedings of the International Conference on Machine Learning (ICML), 2008
- Filip Radlinski, Andrei Broder, Peter Ciccolo, Evgeniy Gabrilovich, Vanja Josifovski, and Lance Riedel, Optimizing Relevance and Revenue in Ad Search: A Query Substitution Approach, in Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), ACM, 2008
2007
- Thorsten Joachims and Filip Radlinski, Search Engines that Learn from Implicit Feedback, in Computer, vol. 40, no. 8, pp. 34–40, Institute of Electrical and Electronics Engineers, Inc., August 2007
- Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke, Filip Radlinski, and Geri Gay, Evaluating the Accuracy of Implicit Feedback from Clicks and Query Reformulations in Web Search, in ACM Transactions on Information Systems (TOIS), vol. 25, no. 2, April 2007
- Stefan Pohl, Filip Radlinski, and Thorsten Joachims, Recommending Related Papers Based on Digital Library Access Records, in Proceedings of Joint Conference on Digital Libraries (JCDL), 2007
- Filip Radlinski, Addressing Malicious Noise in Clickthrough Data, in Learning to Rank for Information Retrieval Workshop at SIGIR, 2007
- Filip Radlinski and Thorsten Joachims, Active Exploration for Learning Rankings from Clickthrough Data, in Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2007
- Yisong Yue, Thomas Finley, Filip Radlinski, and Thorsten Joachims, A Support Vector Method for Optimizing Average Precision, in Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), ACM, 2007
2006
- Filip Radlinski and Thorsten Joachims, Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs, in Proceedings of the 21st National Conference on Artificial Intelligence (AAAI), 2006
- Filip Radlinski and Susan Dumais, Improving Personalized Web Search using Result Diversification, in Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), ACM, 2006
2005
- Filip Radlinski and Thorsten Joachims, Evaluating the Robustness of Learning from Implicit Feedback, in ICML Workshop on Learning In Web Search, 2005
- Filip Radlinski and Thorsten Joachims, Query Chains: Learning to Rank from Implicit Feedback, in Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2005
- Eric Loken, Filip Radlinski, Vincent H. Crespi, and Josh Millet, New SAT Is to Old SAT as ..., in APS Observer, vol. 18, pp. 15–16, 2005
2004
- Eric Loken, Filip Radlinski, Vincent H. Crespi, Lesleigh Cushing, and Josh Millet, Online study behavior of 100,000 students studying for the SAT, ACT and GRE, in Journal of Educational Computing Research, vol. 30, pp. 255–262, 2004
