World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
9028
World Ranking
9149
National Ranking
3893

Overview

Filip Radlinski is affiliated with Google in the United States and conducts research primarily within the field of Computer Science, with a focus on Artificial Intelligence, Information Systems, and Communication. Their scholarly work intersects with various subfields including Computer Vision and Pattern Recognition as well as Developmental and Educational Psychology.

The main topics that characterize Radlinski's research include:

  • Topic Modeling
  • Recommender Systems and Techniques
  • Speech and Dialogue Systems
  • Wikis in Education and Collaboration
  • Multimodal Machine Learning Applications
  • Innovative Teaching and Learning Methods
  • AI in Service Interactions

Radlinski has contributed to several recent publications. Notable papers include:

  • "Conversational Information Seeking," 2023, published in Foundations and Trends® in Information Retrieval
  • "Conversational Information Seeking," 2022, Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
  • "On Natural Language User Profiles for Transparent and Scrutable Recommendation," 2022, Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
  • "Subjective Attributes in Conversational Recommendation Systems: Challenges and Opportunities," 2022, Proceedings of the AAAI Conference on Artificial Intelligence
  • "Generating Usage-related Questions for Preference Elicitation in Conversational Recommender Systems," 2023, ACM Transactions on Recommender Systems

Filip Radlinski has collaborated frequently with several researchers in the field. Frequent co-authors include Krisztian Balog, Hamed Zamani, Johanne R. Trippas, Jeff Dalton, and John Palowitch. These collaborations span multiple research projects and publications.

Their research has appeared predominantly in venues such as arXiv (Cornell University), the Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, Foundations and Trends® in Information Retrieval, ACM Transactions on Recommender Systems, and the Proceedings of the AAAI Conference on Artificial Intelligence. Among these, arXiv hosts the majority of their publications.

Radlinski's body of work addresses several core components of modern AI and information retrieval ecosystems, including conversational recommender systems, user profiling via natural language, and the modeling of subjective attributes within recommendation contexts. Their interdisciplinary reach extends to educational methodologies and multimodal machine learning, reflecting engagement with evolving domains within computer science research.

Best Publications

  • A support vector method for optimizing average precision

    Yisong Yue;Thomas Finley;Filip Radlinski;Thorsten Joachims

  • Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search

    Thorsten Joachims;Laura Granka;Bing Pan;Helene Hembrooke

  • Query chains: learning to rank from implicit feedback

    Filip Radlinski;Thorsten Joachims

  • Learning diverse rankings with multi-armed bandits

    Filip Radlinski;Robert Kleinberg;Thorsten Joachims

  • How does clickthrough data reflect retrieval quality

    Filip Radlinski;Madhu Kurup;Thorsten Joachims

  • Towards Conversational Recommender Systems

    Konstantina Christakopoulou;Filip Radlinski;Katja Hofmann

  • A Theoretical Framework for Conversational Search

    Filip Radlinski;Nick Craswell

  • Improving personalized web search using result diversification

    Filip Radlinski;Susan Dumais

  • Personalizing web search using long term browsing history

    Nicolaas Matthijs;Filip Radlinski

  • Search Engines that Learn from Implicit Feedback

    T. Joachims;F. Radlinski

  • Large-scale validation and analysis of interleaved search evaluation

    Olivier Chapelle;Thorsten Joachims;Filip Radlinski;Yisong Yue

  • Active exploration for learning rankings from clickthrough data

    Filip Radlinski;Thorsten Joachims

  • Mortal Multi-Armed Bandits

    Deepayan Chakrabarti;Ravi Kumar;Filip Radlinski;Eli Upfal

  • Inferring and using location metadata to personalize web search

    Paul N. Bennett;Filip Radlinski;Ryen W. White;Emine Yilmaz

  • Redundancy, diversity and interdependent document relevance

    Filip Radlinski;Paul N. Bennett;Ben Carterette;Thorsten Joachims

  • Online Evaluation for Information Retrieval

    Katja Hofmann;Lihong Li;Filip Radlinski

  • Transparent, Scrutable and Explainable User Models for Personalized Recommendation

    Krisztian Balog;Filip Radlinski;Shushan Arakelyan

  • Optimizing relevance and revenue in ad search: a query substitution approach

    Filip Radlinski;Andrei Broder;Peter Ciccolo;Evgeniy Gabrilovich

  • Inferring query intent from reformulations and clicks

    Filip Radlinski;Martin Szummer;Nick Craswell

  • TREC Complex Answer Retrieval Overview.

    Laura Dietz;Manisha Verma;Filip Radlinski;Nick Craswell

  • Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

    Paul N. Bennett;Vanja Josifovski;Jennifer Neville;Filip Radlinski

Frequent Co-Authors

Thorsten Joachims
Thorsten Joachims Cornell University
Nick Craswell
Nick Craswell Microsoft (United States)
Paul N. Bennett
Paul N. Bennett Microsoft (United States)
Aleksandrs Slivkins
Aleksandrs Slivkins Microsoft (United States)
Ryen W. White
Ryen W. White Microsoft (United States)
Krisztian Balog
Krisztian Balog University of Stavanger
Yisong Yue
Yisong Yue California Institute of Technology
Yoram Bachrach
Yoram Bachrach DeepMind (United Kingdom)
Vincent H. Crespi
Vincent H. Crespi Pennsylvania State University

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