Filip Radlinski

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Thorsten JoachimsProfessor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
Nick CraswellMicrosoftVerified email at microsoft.com
Krisztian BalogUniversity of Stavanger & Google DeepMindVerified email at uis.no
Katja HofmannMicrosoft ResearchVerified email at microsoft.com
Milad ShokouhiMicrosoftVerified email at microsoft.com
Paul N. BennettDirector of Research, Large Language Models, SpotifyVerified email at spotify.com
Emine YilmazUniversity College LondonVerified email at ucl.ac.uk
Yisong YueCalifornia Institute of Technology; Asari AI; Latitude AIVerified email at caltech.edu
Ryen W. WhiteGeneral Manager, Deputy Lab Director, Microsoft ResearchVerified email at microsoft.com
Laura GrankaGoogle, IncVerified email at google.com
Susan DumaisTechnical Fellow, Microsoft ResearchVerified email at microsoft.com
Robert KleinbergDepartment of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
Hamed ZamaniAssociate Professor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
Aleksandrs SlivkinsSenior Principal Researcher, Microsoft Research NYCVerified email at microsoft.com
Madhu KurupVice PresidentVerified email at amazon.com
Geri GayProfessor of Information Science and Communication, Cornell UniversityVerified email at cornell.edu
Bing PanPenn State UniversityVerified email at psu.edu
Johanne R. TrippasVice-Chancellor's Research Fellow, School of Computing Technologies, RMIT UniversityVerified email at rmit.edu.au
Jeff DaltonAssociate Professor (Reader) at University of EdinburghVerified email at ed.ac.uk
Sreenivas GollapudiGoogleVerified email at google.com

Filip Radlinski

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Verified email at google.com

Information Retrieval Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
A support vector method for optimizing average precision Y Yue, T Finley, F Radlinski, T Joachims Proceedings of the 30th annual international ACM SIGIR conference on …, 2007	911	2007
Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search T Joachims, L Granka, B Pan, H Hembrooke, F Radlinski, G Gay ACM Transactions on Information Systems (TOIS) 25 (2), 7-es, 2007	823	2007
Query chains: learning to rank from implicit feedback F Radlinski, T Joachims Proceedings of the eleventh ACM SIGKDD international conference on Knowledge …, 2005	699	2005
Learning diverse rankings with multi-armed bandits F Radlinski, R Kleinberg, T Joachims Proceedings of the 25th international conference on Machine learning, 784-791, 2008	611	2008
Towards conversational recommender systems K Christakopoulou, F Radlinski, K Hofmann Proceedings of the 22nd ACM SIGKDD international conference on knowledge …, 2016	479	2016
A theoretical framework for conversational search F Radlinski, N Craswell Proceedings of the 2017 conference on conference human information …, 2017	427	2017
How does clickthrough data reflect retrieval quality? F Radlinski, M Kurup, T Joachims Proceedings of the 17th ACM conference on Information and knowledge …, 2008	411	2008
Improving personalized web search using result diversification F Radlinski, S Dumais Proceedings of the 29th annual international ACM SIGIR conference on …, 2006	348	2006
Personalizing web search using long term browsing history N Matthijs, F Radlinski Proceedings of the fourth ACM international conference on Web search and …, 2011	277	2011
Search engines that learn from implicit feedback T Joachims, F Radlinski Computer 40 (8), 34-40, 2007	251	2007
Large-scale validation and analysis of interleaved search evaluation O Chapelle, T Joachims, F Radlinski, Y Yue ACM Transactions on Information Systems (TOIS) 30 (1), 1-41, 2012	229	2012
Active exploration for learning rankings from clickthrough data F Radlinski, T Joachims Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007	220	2007
Mortal multi-armed bandits D Chakrabarti, R Kumar, F Radlinski, E Upfal Advances in neural information processing systems 21, 2008	208	2008
Transparent, Scrutable and Explainable User Models for Personalized Recommendation K Balog, F Radlinski, S Arakelyan The 42nd International ACM SIGIR Conference on Research and Development in …, 2019	181	2019
TREC Complex Answer Retrieval Overview. L Dietz, M Verma, F Radlinski, N Craswell TREC, 2017	162	2017
Ranked bandits in metric spaces: learning diverse rankings over large document collections A Slivkins, F Radlinski, S Gollapudi The Journal of Machine Learning Research 14 (1), 399-436, 2013	161*	2013
Redundancy, diversity and interdependent document relevance F Radlinski, PN Bennett, B Carterette, T Joachims ACM SIGIR Forum 43 (2), 46-52, 2009	161	2009
Inferring and using location metadata to personalize web search PN Bennett, F Radlinski, RW White, E Yilmaz Proceedings of the 34th international ACM SIGIR conference on Research and …, 2011	157	2011
Online evaluation for information retrieval K Hofmann, L Li, F Radlinski Foundations and Trends® in Information Retrieval 10 (1), 1-117, 2016	140	2016
Inferring query intent from reformulations and clicks F Radlinski, M Szummer, N Craswell Proceedings of the 19th international conference on World wide web, 1171-1172, 2010	140	2010

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