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 current research is focused on conversational recommendation and search, machine learning and large scale applications. My interests include evaluation of online systems, personalized search, and learning to rank.
- Conversational Search and Recommendation
- K. Balog, F. Radlinski and S. Arakelyan, Transparent, Scrutable and Explainable User Models for Personalized Recommendation, SIGIR 2019.
- F. Radlinski, K. Balog, B. Byrne, K. Krishnamoorthi, Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences, SIGDIAL 2019.
[Data; Blog post]
- F. Radlinski, N. Craswell, A Theoretical Framework for Conversational Search, CHIIR 2017.
- 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
- 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.
- 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.
- 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.