Thorsten Joachims
Impact in
- Artificial Intelligence top 0.01%
- Topic Modeling
- Text and Document Classification Technologies
- Natural Language Processing Techniques
- Computer Vision and Pattern Recognition top 0.05%
- Advanced Image and Video Retrieval Techniques
- Face and Expression Recognition
Papers in
-
- Machine Learning and Algorithms 32
- Topic Modeling 21
- Text and Document Classification Technologies 16
- Advanced Text Analysis Techniques 14
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- Information Retrieval and Search Behavior 29
- Recommender Systems and Techniques 22
- Co-authors
- Filip Radlinski (11 shared papers)Laura Granka (7 shared papers)Geri Gay (7 shared papers)Thomas Hofmann (3 shared papers)Yasemin Altün (2 shared papers)Ioannis Tsochantaridis (2 shared papers)Thomas Finley (5 shared papers)Chun-Nam Yu (5 shared papers)
- Journals
- ACM SIGIR Forum (6 papers)ACM Transactions on Information Systems (2 papers)Machine Learning (2 papers)The International Journal of Robotics Research (2 papers)Journal of Machine Learning Research (2 papers)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Thorsten Joachims
153 papers receiving 23.4k citations
Thorsten Joachims's Hit Papers
Peers
Comparison fields: 5 of 205
- Artificial Intelligence 14.7k
- Computer Vision and Pattern Recognition 7.3k
- Information Systems 7.9k
- Signal Processing 2.3k
- Computer Science Applications 982
Countries citing papers authored by Thorsten Joachims
This map shows the geographic impact of Thorsten Joachims's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Thorsten Joachims with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thorsten Joachims more than expected).
Fields of papers citing papers by Thorsten Joachims
This network shows the impact of papers produced by Thorsten Joachims. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Thorsten Joachims. The network helps show where Thorsten Joachims may publish in the future.
Co-authors
The 25 scholars most cited alongside Thorsten Joachims, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 161 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Optimizing search engines using clickthrough data Hit paper breakdown → | 2002 | 2543 |
| 2 | Making Large-Scale SVM Learning Practical Hit paper breakdown → | 2006 | 2409 |
| 3 | Transductive Inference for Text Classification using Support Vector Machines Hit paper breakdown → | 1999 | 1818 |
| 4 | Training linear SVMs in linear time Hit paper breakdown → | 2006 | 1295 |
| 5 | Large Margin Methods for Structured and Interdependent Output Variables Hit paper breakdown → | 2005 | 1226 |
| 6 | Learning to Classify Text Using Support Vector Machines Hit paper breakdown → | 2002 | 895 |
| 7 | Accurately interpreting clickthrough data as implicit feedback Hit paper breakdown → | 2005 | 840 |
| 8 | Support vector machine learning for interdependent and structured output spaces Hit paper breakdown → | 2004 | 831 |
| 9 | A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization Hit paper breakdown → | 1997 | 789 |
| 10 | Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms Hit paper breakdown → | 2002 | 644 |
| 11 | Cutting-plane training of structural SVMs Hit paper breakdown → | 2009 | 610 |
| 12 | A support vector method for multivariate performance measures Hit paper breakdown → | 2005 | 512 |
| 13 | In Google We Trust: Users’ Decisions on Rank, Position, and Relevance Hit paper breakdown → | 2007 | 489 |
| 14 | Eye-tracking analysis of user behavior in WWW search Hit paper breakdown → | 2004 | 475 |
| 15 | A support vector method for optimizing average precision Hit paper breakdown → | 2007 | 459 |
| 16 | Web Watcher: A Tour Guide for the World Wide Web. | 1997 | 424 |
| 17 | Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search Hit paper breakdown → | 2007 | 412 |
| 18 | Transductive learning via spectral graph partitioning | 2003 | 393 |
| 19 | Learning structural SVMs with latent variables Hit paper breakdown → | 2009 | 390 |
| 20 | Learning a Distance Metric from Relative Comparisons | 2003 | 366 |
About Thorsten Joachims
Thorsten Joachims is a scholar working on Artificial Intelligence, Information Systems, Management Science and Operations Research, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 161 papers that have together received 25.3k indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (35 papers), Machine Learning and Algorithms (32 papers), Information Retrieval and Search Behavior (29 papers), Recommender Systems and Techniques (22 papers), Topic Modeling (21 papers), Text and Document Classification Technologies (16 papers), Advanced Text Analysis Techniques (14 papers) and Mobile Crowdsensing and Crowdsourcing (14 papers). The work is most often cited by research in Artificial Intelligence (14.7k citations), Computer Vision and Pattern Recognition (7.3k citations), Information Systems (7.9k citations), Signal Processing (2.3k citations) and Computer Science Applications (982 citations). Thorsten Joachims has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Filip Radlinski, Laura Granka, Geri Gay, Thomas Hofmann, Yasemin Altün, Ioannis Tsochantaridis, Thomas Finley, Chun-Nam Yu, Bing Pan and Helene Hembrooke. Their work appears in journals such as ACM SIGIR Forum, ACM Transactions on Information Systems, Machine Learning, The International Journal of Robotics Research and Journal of Machine Learning Research.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.