World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
40
Citations
9028
World Ranking
9148
National Ranking
3893

Filip Radlinski publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Filip Radlinski sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 91 publications — 6th percentile

6% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Filip Radlinski D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Filip Radlinski sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 40 D-Index — 37th percentile

37% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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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