I am a research scientist at Google in London, UK. Previously I was an Applied Researcher at Microsoft in Cambridge, UK. I completed by PhD in Computer Science at Cornell University.
My research is focused on information retrieval, machine learning and large scale applications. My interests include conversational search, evaluation of online systems, personalized search, and learning to rank.
- On Click-Based Evaluation
- K. Hofmann, L. Li and F Radlinski, Online Evaluation for Information Retrieval . Foundations and Trends in Information Retrieval (FTIR), June 2016.
- 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.
- Conversational and Mobile Search
- K. Christakopoulou, F. Radlinski, K. Hofmann, Towards Conversational Recommender Systems, KDD 2016.
- Sandro Bauer, Filip Radlinski, Ryen White, Where Can I Buy a Boulder? Searching for Offline Retail Locations , WWW 2016
- 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.
- Learning from Online Data
E. Yilmaz, M. Verma, N. Craswell, F. Radlinski and P. Bailey. Relevance and Effort: An Analysis of Document Utility. CIKM 2014
- 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.