Filip Radlinski
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Microsoft Research 21 Station Road Cambridge CB1 2FB United Kingdom
Email: filiprad [at] microsoft.com Phone: +44 1223 479931 |
I am an applied researcher at Microsoft, and work for Bing. I am at Microsoft Research Cambridge, in the Machine Learning and Perception group, and the Online Services and Advertising 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 personalization, diversity in web search and web scale online learning algorithms.
Before joining Microsoft in 2008, I completed my PhD in Computer Science at Cornell University. I also have a personal homepage.
Publications
- Filip Radlinski and Nick Craswell, Optimized Interleaving for Online Retrieval Evaluation, in ACM International Conference on Web Search and Data Mining (WSDM) - Best Paper Award, ACM, February 2013
- Katja Hofmann, Fritz Behr, and Filip Radlinski, On Caption Bias in Interleaving Experiments, in Proceedings of the ACM Conference on Information and Knowledge Management (CIKM), ACM, October 2012
- Olivier Chapelle, Thorsten Joachims, Filip Radlinski, and Yisong Yue, Large Scale Validation and Analysis of Interleaved Search Evaluation, in Transactions on Information Systems (TOIS), vol. 30, no. 1, ACM, February 2012
- Paul N. Bennett, Filip Radlinski, Ryen White, and Emine Yilmaz, Inferring and Using Location Metadata to Personalize Web Search, in Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR), Association for Computing Machinery, Inc., July 2011
- Nicolaas Matthijs and Filip Radlinski, Personalizing Web Search using Long Term Browsing History, in ACM International Conference on Web Search and Data Mining (WSDM), Association for Computing Machinery, Inc., February 2011
- Filip Radlinski, Paul N. Bennett, and Emine Yilmaz, Detecting Duplicate Web Documents using Clickthrough Data, in ACM International Conference on Web Search and Data Mining (WSDM), Association for Computing Machinery, Inc., February 2011
- Filip Radlinski, Martin Szummer, and Nick Craswell, Metrics for Assessing Sets of Subtopics, in SIGIR Conf. Research and Development in Information Retrieval, Association for Computing Machinery, Inc., July 2010
- Filip Radlinski and Nick Craswell, Comparing the Sensitivity of Information Retrieval Metrics, in Proceedings of SIGIR, Association for Computing Machinery, Inc., July 2010
- Alex Slivkins, Filip Radlinski, and Sreenivas Gollapudi, Learning optimally diverse rankings over large document collections, in Proc. of the 27th International Conference on Machine Learning (ICML 2010), 21 June 2010
- Filip Radlinski, Martin Szummer, and Nick Craswell, Inferring Query Intent from Reformulations and Clicks, in Proc. 19th Annual International World Wide Web Conference (WWW '10)., Association for Computing Machinery, Inc., April 2010
- Filip Radlinski, Madhu Kurup, and Thorsten Joachims, Evaluating Search Engine Relevance with Click-Based Metrics, in Preference Learning, pp. 337-362, Springer Verlag, 2010
- Filip Radlinski, Paul N. Bennett, Ben Carterette, and Thorsten Joachims, Redundancy, Diversity, and Interdependent Document Relevance, a summary of the SIGIR 2009 workshop, in SIGIR Forum, December 2009
- Deepayan Chakrabarti, Ravi Kumar, Filip Radlinski, and Eli Upfal, Mortal Multi-Armed Bandits, in Advances in Neural Information Processing Systems (NIPS), 2009
- 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, 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, 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
- 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
- Filip Radlinski, Addressing Malicious Noise in Clickthrough Data, in Learning to Rank for Information Retrieval Workshop at SIGIR, 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
- 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
- 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 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
- 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 Thorsten Joachims, Evaluating the Robustness of Learning from Implicit Feedback, in ICML Workshop on Learning In Web Search, 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
- 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, 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
