2026 Lars Schmidt-Thieme: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	53	4688	4556	211	204	289	21100

Overview

Lars Schmidt-Thieme is affiliated with the University of Hildesheim in Germany. Their research contributions primarily lie within the field of Computer Science, with significant work in subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Management Science and Operations Research, and Industrial and Manufacturing Engineering.

The scientist's research topics include:

Time Series Analysis and Forecasting
Domain Adaptation and Few-Shot Learning
Machine Learning and Data Classification
Stock Market Forecasting Methods
Vehicle Routing Optimization Methods
Transportation and Mobility Innovations
Forecasting Techniques and Applications

Frequent coauthors collaborating with Lars Schmidt-Thieme are:

Vijaya Krishna Yalavarthi
Jonas K. Falkner
Kiran Madhusudhanan
Johannes Burchert
Stefan Born

Lars Schmidt-Thieme has published in multiple venues with a focus on:

arXiv (Cornell University)
Proceedings of the AAAI Conference on Artificial Intelligence
International Journal of Data Science and Analytics
Data Mining and Knowledge Discovery
2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)

Selected recent papers authored or coauthored include:

"Scalable Event-Based Clustering of Social Media Via Record Linkage Techniques" (2021), published in Proceedings of the International AAAI Conference on Web and Social Media
"When bioprocess engineering meets machine learning: A survey from the perspective of automated bioprocess development" (2022), published in Biochemical Engineering Journal
"Learning to Solve Vehicle Routing Problems with Time Windows through Joint Attention" (2020), published in arXiv (Cornell University)
"Classification of Sparse Time Series via Supervised Matrix Factorization" (2021), published in Proceedings of the AAAI Conference on Artificial Intelligence

Best Publications

BPR: Bayesian personalized ranking from implicit feedback

Steffen Rendle;Christoph Freudenthaler;Zeno Gantner;Lars Schmidt-Thieme

5624
Citations
Factorizing personalized Markov chains for next-basket recommendation

Steffen Rendle;Christoph Freudenthaler;Lars Schmidt-Thieme

2646
Citations
Pairwise interaction tensor factorization for personalized tag recommendation

Steffen Rendle;Lars Schmidt-Thieme

861
Citations
Tag Recommendations in Folksonomies

Robert Jäschke;Leandro Marinho;Andreas Hotho;Lars Schmidt-Thieme

750
Citations
Fast context-aware recommendations with factorization machines

Steffen Rendle;Zeno Gantner;Christoph Freudenthaler;Lars Schmidt-Thieme

703
Citations
Tag-aware recommender systems by fusion of collaborative filtering algorithms

Karen H. L. Tso-Sutter;Leandro Balby Marinho;Lars Schmidt-Thieme

523
Citations
Learning time-series shapelets

Josif Grabocka;Nicolas Schilling;Martin Wistuba;Lars Schmidt-Thieme

519
Citations
MyMediaLite: a free recommender system library

Zeno Gantner;Steffen Rendle;Christoph Freudenthaler;Lars Schmidt-Thieme

505
Citations
Learning optimal ranking with tensor factorization for tag recommendation

Steffen Rendle;Leandro Balby Marinho;Alexandros Nanopoulos;Lars Schmidt-Thieme

467
Citations
Cost-sensitive learning methods for imbalanced data

Nguyen Thai-Nghe;Zeno Gantner;Lars Schmidt-Thieme

390
Citations
Recommender system for predicting student performance

Nguyen Thai-Nghe;Lucas Drumond;Artus Krohn-Grimberghe;Lars Schmidt-Thieme

381
Citations
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations

Zeno Gantner;Lucas Drumond;Christoph Freudenthaler;Steffen Rendle

368
Citations
Learning Taxonomic Relations from Heterogeneous Sources of Evidence

Philipp Cimiano;Aleksander Pivk;Lars Schmidt-Thieme;Steffen Staab

319
Citations
Taxonomy-driven computation of product recommendations

Cai-Nicolas Ziegler;Georg Lausen;Lars Schmidt-Thieme

290
Citations
Online-updating regularized kernel matrix factorization models for large-scale recommender systems

Steffen Rendle;Lars Schmidt-Thieme

289
Citations
Data Analysis, Machine Learning, and Applications

Christine Preisach;Hans Burkhardt;Lars Schmidt-Thieme;Reinhold Decker

288
Citations
Tag recommendations in social bookmarking systems

Robert Jäschke;Leandro Marinho;Andreas Hotho;Lars Schmidt-Thieme

271
Citations
Beyond Manual Tuning of Hyperparameters

Frank Hutter;Jörg Lücke;Lars Schmidt-Thieme

236
Citations
Multi-relational matrix factorization using bayesian personalized ranking for social network data

Artus Krohn-Grimberghe;Lucas Drumond;Christoph Freudenthaler;Lars Schmidt-Thieme

223
Citations
Real-time top-n recommendation in social streams

Ernesto Diaz-Aviles;Lucas Drumond;Lars Schmidt-Thieme;Wolfgang Nejdl

198
Citations
Proceedings of the 2009 ACM Conference on Recommender Systems, RecSys

Lawrence D. Bergman;Alexander Tuzhilin;Robin Burke;Alexander Felfernig

33
Citations

Frequent Co-Authors

Alexandros Nanopoulos University of Hildesheim

Andreas Hotho University of Würzburg

Gerd Stumme University of Kassel

Philipp Cimiano Bielefeld University

Wolfgang Nejdl University of Hannover

Steffen Staab University of Stuttgart

Alexander Felfernig Graz University of Technology

Panagiotis Symeonidis University of the Aegean

Alexander Tuzhilin New York University

York Sure Karlsruhe Institute of Technology

External Links

Personal website of Lars Schmidt-Thieme Google Scholar page

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

Exploring computer science in the USA opens doors to a variety of related online degrees and career pathways. Many students expand their skill set by pursuing further education in fields like online data science degree programs, which provide specialized knowledge highly valued in today’s data-driven industries.

For those interested in leadership or management careers, options such as cheapest mba online programs can provide valuable business insights at an affordable cost. Accelerated programs like the 1 year master programs and the 2 year construction management degree make it possible to gain advanced qualifications in a shorter timeframe.

Choosing an online pathway offers flexibility for working professionals and those balancing multiple responsibilities. Students can now tailor their studies to fit their unique schedules, while earning degrees that connect directly to in-demand career opportunities in technology, business, and beyond.