I am an applied researcher at Microsoft Research in Cambridge, UK. I completed by PhD in Computer Science at Cornell University.
My research is focused using machine learning to learn from data collected implicitly from web users. My interests include personalized search, learning to rank, advertising as an optimization problem and how to leverage the internet to help students learn. [MORE]
- On Click-Based Evaluation
- Tutorial at ECIR 2013 with Katja Hofmann: Practical Online Retrieval Evaluation.
- F. Radlinski, N. Craswell, Optimized Interleaving for Online Retrieval Evaluation, WSDM 2013 (best paper award).
- O. Chapelle, T. Joachims, F. Radlinski and Y. Yue, Large Scale Validation and Analysis of Interleaved Search Evaluation, ACM TOIS, 2012.
- F. Radlinski and P.N. Bennett and E. Yilmaz, Detecting Duplicate Web Documents using Clickthrough Data, WSDM 2011.
- On Personalization and Diversity
- N. Matthijs and F. Radlinski, Personalizing Web Search using Long Term Browsing History, WSDM 2011.
- F. Radlinski, M. Szummer and N. Craswell, Inferring Query Intent from Reformulations and Clicks, WWW 2010.
- F. Radlinski, P.N. Bennett, B. Carterette and T. Joachims, Redundancy, Diversity and Interdependent Document Relevance, a summary of the SIGIR 2009 workshop, SIGIR Forum, Dec 2009.
- On Multi-Armed Bandits
- A. Slivkins, F. Radlinski and S. Gollapudi, Learning Optimally Diverse Rankings over Large Document Collections, ICML 2010. Long version in JMLR 14 (2013) [pdf]
- D. Chakrabarti, R. Kumar, F. Radlinski and E. Upfal, Mortal Multi-Armed Bandits, NIPS 2008.