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ORCIDJöran Beel
Joeran Beel
Person information
- affiliation: University of Siegen, Germany
- affiliation (former): Trinity College Dublin, School of Computer Science and Statistics, ADAPT Centre, Dublin, Ireland
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2020 – today
- 2024
[c72]Tobias Vente
, Lukas Wegmeth, Alan Said, Joeran Beel:
From Clicks to Carbon: The Environmental Toll of Recommender Systems. RecSys 2024: 580-590- [c71]Joeran Beel, Lukas Wegmeth, Lien Michiels, Steffen Schulz:
Informed Dataset Selection with 'Algorithm Performance Spaces'. RecSys 2024: 1085-1090 - [c70]Lukas Wegmeth, Tobias Vente, Joeran Beel:
Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets. RecSys 2024: 1163-1167 - [i40]Tobias Vente, Joeran Beel:
The Potential of AutoML for Recommender Systems. CoRR abs/2402.04453 (2024) - [i39]Tobias Vente, Lukas Wegmeth, Alan Said, Joeran Beel:
From Clicks to Carbon: The Environmental Toll of Recommender Systems. CoRR abs/2408.08203 (2024) - [i38]Lukas Wegmeth, Tobias Vente, Joeran Beel:
Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets. CoRR abs/2409.05461 (2024) - [i37]Lukas Wegmeth, Tobias Vente, Alan Said, Joeran Beel:
EMERS: Energy Meter for Recommender Systems. CoRR abs/2409.15060 (2024) - [i36]Ardalan Arabzadeh, Tobias Vente, Joeran Beel:
Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance. CoRR abs/2410.09359 (2024) - [i35]Christopher Mahlich, Tobias Vente, Joeran Beel:
From Theory to Practice: Implementing and Evaluating e-Fold Cross-Validation. CoRR abs/2410.09463 (2024) - [i34]Moritz Baumgart, Lukas Wegmeth, Tobias Vente, Joeran Beel:
e-Fold Cross-Validation for Recommender-System Evaluation. CoRR abs/2412.01011 (2024) - 2023
- [j8]Christine Bauer, Ben Carterette, Nicola Ferro, Norbert Fuhr, Joeran Beel, Timo Breuer, Charles L. A. Clarke, Anita Crescenzi, Gianluca Demartini, Giorgio Maria Di Nunzio, Laura Dietz, Guglielmo Faggioli, Bruce Ferwerda, Maik Fröbe, Matthias Hagen, Allan Hanbury, Claudia Hauff, Dietmar Jannach, Noriko Kando, Evangelos Kanoulas, Bart P. Knijnenburg, Udo Kruschwitz, Meijie Li, Maria Maistro, Lien Michiels, Andrea Papenmeier, Martin Potthast, Paolo Rosso, Alan Said, Philipp Schaer, Christin Seifert, Damiano Spina, Benno Stein, Nava Tintarev, Julián Urbano, Henning Wachsmuth, Martijn C. Willemsen, Justin Zobel:
Report on the Dagstuhl Seminar on Frontiers of Information Access Experimentation for Research and Education. SIGIR Forum 57(1): 7:1-7:28 (2023) - [c69]Lennart Oswald Purucker, Joeran Beel:
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure. AutoML 2023: 1/1-23 - [c68]Lennart Oswald Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger H. Hoos:
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML. AutoML 2023: 10/1-34 - [c67]Tobias Vente, Michael D. Ekstrand, Joeran Beel:
Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit. RecSys 2023: 1212-1216 - [c66]Lukas Wegmeth, Tobias Vente, Lennart Purucker, Joeran Beel:
The Effect of Random Seeds for Data Splitting on Recommendation Accuracy. Perspectives@RecSys 2023 - [i33]Lennart Purucker, Joeran Beel:
Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML. CoRR abs/2307.00285 (2023) - [i32]Lennart Purucker, Joeran Beel:
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure. CoRR abs/2307.00286 (2023) - [i31]Lennart Purucker, Lennart Schneider, Marie Anastacio, Joeran Beel, Bernd Bischl, Holger H. Hoos:
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML. CoRR abs/2307.08364 (2023) - 2022
- [c65]Lennart Purucker, Felix I. Stamm, Florian Lemmerich, Joeran Beel:
Estimating the Pruned Search Space Size of Subgroup Discovery. ICDM 2022: 1155-1160 - [c64]Lukas Wegmeth, Joeran Beel:
CaMeLS: Cooperative Meta-Learning Service for Recommender Systems. Perspectives@RecSys 2022 - 2021
- [c63]Teresa Scheidt, Joeran Beel:
Time-dependent Evaluation of Recommender Systems. Perspectives@RecSys 2021 - [c62]Joeran Beel, Haley Dixon:
The 'Unreasonable' Effectiveness of Graphical User Interfaces for Recommender Systems. UMAP (Adjunct Publication) 2021: 22-28 - 2020
- [j7]Felix Beierle, Akiko Aizawa, Andrew Collins, Joeran Beel:
Choice overload and recommendation effectiveness in related-article recommendations. Int. J. Digit. Libr. 21(3): 231-246 (2020) - [c61]Rohan Anand, Joeran Beel:
Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization. RecSys 2020: 585-587 - [c60]Joeran Beel:
Recommender-Systems.com: A Central Platform for the Recommender-System Community. RecSys 2020: 600-603 - [i30]Mark Grennan, Joeran Beel:
Synthetic vs. Real Reference Strings for Citation Parsing, and the Importance of Re-training and Out-Of-Sample Data for Meaningful Evaluations: Experiments with GROBID, GIANT and Cora. CoRR abs/2004.10410 (2020) - [i29]Joeran Beel, Bryan Tyrell, Edward Bergman, Andrew Collins, Shahad Nagoor:
Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]. CoRR abs/2006.12328 (2020) - [i28]Rohan Anand, Joeran Beel:
Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization. CoRR abs/2008.13532 (2020) - [i27]Mohammed Al-Rawi, Joeran Beel:
Towards an Interoperable Data Protocol Aimed at Linking the Fashion Industry with AI Companies. CoRR abs/2009.03005 (2020) - [i26]Oisín Carroll, Joeran Beel:
Finite Group Equivariant Neural Networks for Games. CoRR abs/2009.05027 (2020) - [i25]Andrew Collins, Laura Tierney, Joeran Beel:
Per-Instance Algorithm Selection for Recommender Systems via Instance Clustering. CoRR abs/2012.15151 (2020)
2010 – 2019
- 2019
- [c59]Dominika Tkaczyk, Andrew Collins, Joeran Beel:
NaïveRole: Author-Contribution Extraction and Parsing from Biomedical Manuscripts. AICS 2019: 4-15 - [c58]Mark Grennan, Martin Schibel, Andrew Collins, Joeran Beel:
GIANT: The 1-Billion Annotated Synthetic Bibliographic-Reference-String Dataset for Deep Citation Parsing. AICS 2019: 260-271 - [c57]Conor O'Sullivan, Joeran Beel:
Predicting the Outcome of Judicial Decisions made by the European Court of Human Rights. AICS 2019: 272-283 - [c56]Nicholas Bonello, Joeran Beel, Séamus Lawless, Jeremy Debattista:
Multi-stream Data Analytics for Enhanced Performance Prediction in Fantasy Football. AICS 2019: 284-292 - [c55]Joeran Beel, Lars Kotthoff:
Preface: The 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR). AMIR@ECIR 2019: 1-9 - [c54]Gordian Edenhofer, Andrew Collins, Akiko Aizawa, Joeran Beel:
Augmenting the DonorsChoose.org Corpus for Meta-Learning. AMIR@ECIR 2019: 32-38 - [c53]Joeran Beel, Barry Smyth, Andrew Collins:
RARD II: The 94 Million Related-Article Recommendation Dataset. AMIR@ECIR 2019: 39-55 - [c52]Jöran Beel, Andrew Collins, Oliver Kopp, Linus W. Dietz, Petr Knoth:
Online Evaluations for Everyone: Mr. DLib's Living Lab for Scholarly Recommendations. ECIR (2) 2019: 213-219 - [c51]Jöran Beel, Lars Kotthoff:
Proposal for the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR). ECIR (2) 2019: 383-388 - [c50]Andrew Collins, Jöran Beel:
Document Embeddings vs. Keyphrases vs. Terms for Recommender Systems: A Large-Scale Online Evaluation. JCDL 2019: 130-133 - [c49]Mark Collier, Joeran Beel:
Memory-Augmented Neural Networks for Machine Translation. MTSummit (1) 2019: 172-181 - [c48]Hebatallah A. Mohamed Hassan, Giuseppe Sansonetti, Fabio Gasparetti, Alessandro Micarelli, Jöran Beel:
BERT, ELMo, USE and InferSent Sentence Encoders: The Panacea for Research-Paper Recommendation? RecSys (Late-Breaking Results) 2019: 6-10 - [c47]Jöran Beel, Victor Brunel:
Data Pruning in Recommender Systems Research: Best-Practice or Malpractice? RecSys (Late-Breaking Results) 2019: 26-30 - [c46]Andrew Collins, Joeran Beel:
A First Analysis of Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems. ComplexRec@RecSys 2019: 29-34 - [c45]Philipp Scharpf, Ian Mackerracher, Moritz Schubotz, Jöran Beel, Corinna Breitinger, Bela Gipp:
AnnoMath TeX - a formula identifier annotation recommender system for STEM documents. RecSys 2019: 532-533 - [c44]Jöran Beel, Alan Griffin, Conor O'Shea:
Darwin & Goliath: a white-label recommender-system as-a-service with automated algorithm-selection. RecSys 2019: 534-535 - [e2]Jöran Beel, Lars Kotthoff:
Proceedings of the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval co-located with the 41st European Conference on Information Retrieval (ECIR 2019), Cologne, Germany, April 14, 2019. CEUR Workshop Proceedings 2360, CEUR-WS.org 2019 [contents] - [i24]Andrew Collins, Jöran Beel:
Document Embeddings vs. Keyphrases vs. Terms: An Online Evaluation in Digital Library Recommender Systems. CoRR abs/1905.11244 (2019) - [i23]Mark Collier, Joeran Beel:
Memory-Augmented Neural Networks for Machine Translation. CoRR abs/1909.08314 (2019) - [i22]Nicholas Bonello, Joeran Beel, Séamus Lawless, Jeremy Debattista:
Multi-stream Data Analytics for Enhanced Performance Prediction in Fantasy Football. CoRR abs/1912.07441 (2019) - [i21]Andrew Collins, Joeran Beel:
Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems. CoRR abs/1912.08694 (2019) - [i20]Dominika Tkaczyk, Andrew Collins, Joeran Beel:
NaïveRole: Author-Contribution Extraction and Parsing from Biomedical Manuscripts. CoRR abs/1912.10170 (2019) - [i19]Conor O'Sullivan, Joeran Beel:
Predicting the Outcome of Judicial Decisions made by the European Court of Human Rights. CoRR abs/1912.10819 (2019) - 2018
- [c43]Jöran Beel, Andrew Collins, Akiko Aizawa:
Mr. DLib's Architecture for Scholarly Recommendations-as-a-Service. AICS 2018: 78-89 - [c42]Mark Collier, Jöran Beel:
An Empirical Comparison of Syllabuses for Curriculum Learning. AICS 2018: 150-161 - [c41]Dominika Tkaczyk, Rohit Gupta, Riccardo Cinti, Jöran Beel:
ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers. AICS 2018: 162-173 - [c40]Andrew Collins, Dominika Tkaczyk, Jöran Beel:
A Novel Approach to Recommendation Algorithm Selection using Meta-Learning. AICS 2018: 210-219 - [c39]Mark Collier, Jöran Beel:
Implementing Neural Turing Machines. ICANN (3) 2018: 94-104 - [c38]Andrew Collins, Dominika Tkaczyk, Akiko Aizawa, Jöran Beel:
Position Bias in Recommender Systems for Digital Libraries. iConference 2018: 335-344 - [c37]Dominika Tkaczyk, Andrew Collins, Paraic Sheridan, Jöran Beel:
Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parsers. JCDL 2018: 99-108 - [c36]Dominika Tkaczyk, Andrew Collins, Jöran Beel:
Who Did What?: Identifying Author Contributions in Biomedical Publications using Naïve Bayes. JCDL 2018: 387-388 - [e1]Rob Brennan, Jöran Beel, Ruth Byrne, Jeremy Debattista, Ademar Crotti Junior:
Proceedings for the 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science Trinity College Dublin, Dublin, Ireland, December 6-7th, 2018. CEUR Workshop Proceedings 2259, CEUR-WS.org 2018 [contents] - [i18]Dominika Tkaczyk, Andrew Collins, Paraic Sheridan, Jöran Beel:
Evaluation and Comparison of Open Source Bibliographic Reference Parsers: A Business Use Case. CoRR abs/1802.01168 (2018) - [i17]Dominika Tkaczyk, Andrew Collins, Jöran Beel:
A Method for Discovering and Extracting Author Contributions Information from Scientific Biomedical Publications. CoRR abs/1802.01174 (2018) - [i16]Andrew Collins, Dominika Tkaczyk, Akiko Aizawa, Jöran Beel:
A Study of Position Bias in Digital Library Recommender Systems. CoRR abs/1802.06565 (2018) - [i15]Andrew Collins, Jöran Beel, Dominika Tkaczyk:
One-at-a-time: A Meta-Learning Recommender-System for Recommendation-Algorithm Selection on Micro Level. CoRR abs/1805.12118 (2018) - [i14]Jöran Beel, Barry Smyth, Andrew Collins:
RARD II: The 2nd Related-Article Recommendation Dataset. CoRR abs/1807.06918 (2018) - [i13]Jöran Beel, Andrew Collins, Oliver Kopp, Linus W. Dietz, Petr Knoth:
Mr. DLib's Living Lab for Scholarly Recommendations. CoRR abs/1807.07298 (2018) - [i12]Mark Collier, Jöran Beel:
Implementing Neural Turing Machines. CoRR abs/1807.08518 (2018) - [i11]Dominika Tkaczyk, Paraic Sheridan, Jöran Beel:
ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing. CoRR abs/1808.09036 (2018) - [i10]Mark Collier, Jöran Beel:
An Empirical Comparison of Syllabuses for Curriculum Learning. CoRR abs/1809.10789 (2018) - [i9]Jöran Beel, Andrew Collins, Akiko Aizawa:
The Architecture of Mr. DLib's Scientific Recommender-System API. CoRR abs/1811.10364 (2018) - [i8]Dominika Tkaczyk, Rohit Gupta, Riccardo Cinti, Jöran Beel:
ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers. CoRR abs/1811.10369 (2018) - 2017
- [j6]Jöran Beel, Zeljko Carevic, Johann Schaible, Gábor Neusch:
RARD: The Related-Article Recommendation Dataset. D Lib Mag. 23(7/8) (2017) - [j5]Bela Gipp, Norman Meuschke, Jöran Beel, Corinna Breitinger:
Using the Blockchain of Cryptocurrencies for Timestamping Digital Cultural Heritage. Bull. IEEE Tech. Comm. Digit. Libr. 13(1) (2017) - [c35]Jöran Beel, Siddarth Dinesh:
Real-World Recommender Systems for Academia: The Pain and Gain in Building, Operating, and Researching them. BIR@ECIR 2017: 6-17 - [c34]Felix Beierle, Akiko Aizawa, Jöran Beel:
Exploring Choice Overload in Related-Article Recommendations in Digital Libraries. BIR@ECIR 2017: 51-61 - [c33]Stefan Langer, Jöran Beel:
Apache Lucene as Content-Based-Filtering Recommender System: 3 Lessons Learned. BIR@ECIR 2017: 85-92 - [c32]Stefan P. Feyer, Sophie Siebert, Bela Gipp, Akiko Aizawa, Jöran Beel:
Integration of the Scientific Recommender System Mr. DLib into the Reference Manager JabRef. ECIR 2017: 770-774 - [c31]Jöran Beel, Siddharth Dinesh, Philipp Mayr, Zeljko Carevic, Raghvendra Jain:
Stereotype and Most-Popular Recommendations in the Digital Library Sowiport. ISI 2017: 96-108 - [c30]Bela Gipp, Corinna Breitinger, Norman Meuschke, Jöran Beel:
CryptSubmit: Introducing Securely Timestamped Manuscript Submission and Peer Review Feedback Using the Blockchain. JCDL 2017: 273-276 - [c29]Jöran Beel, Akiko Aizawa, Corinna Breitinger, Bela Gipp:
Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia. JCDL 2017: 313-314 - [i7]Stefan Langer, Jöran Beel:
Apache Lucene as Content-Based-Filtering Recommender System: 3 Lessons Learned. CoRR abs/1703.08855 (2017) - [i6]Jöran Beel, Bela Gipp, Akiko Aizawa:
Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia. CoRR abs/1703.09108 (2017) - [i5]Jöran Beel:
Towards Effective Research-Paper Recommender Systems and User Modeling based on Mind Maps. CoRR abs/1703.09109 (2017) - [i4]Jöran Beel, Siddharth Dinesh:
Real-World Recommender Systems for Academia: The Pain and Gain in Building, Operating, and Researching them [Long Version]. CoRR abs/1704.00156 (2017) - [i3]Felix Beierle, Akiko Aizawa, Jöran Beel:
Exploring Choice Overload in Related-Article Recommendations in Digital Libraries. CoRR abs/1704.00393 (2017) - [i2]Jöran Beel, Zeljko Carevic, Johann Schaible, Gábor Neusch:
RARD: The Related-Article Recommendation Dataset. CoRR abs/1706.03428 (2017) - [i1]Jöran Beel:
It's Time to Consider "Time" when Evaluating Recommender-System Algorithms [Proposal]. CoRR abs/1708.08447 (2017) - 2016
- [j4]Jöran Beel, Bela Gipp, Stefan Langer, Corinna Breitinger:
Research-paper recommender systems: a literature survey. Int. J. Digit. Libr. 17(4): 305-338 (2016) - [j3]Jöran Beel, Corinna Breitinger, Stefan Langer, Andreas Lommatzsch, Bela Gipp:
Towards reproducibility in recommender-systems research. User Model. User Adapt. Interact. 26(1): 69-101 (2016) - [c28]Andreas Weiler, Jöran Beel, Bela Gipp, Michael Grossniklaus:
Stability Evaluation of Event Detection Techniques for Twitter. IDA 2016: 368-380 - 2015
- [b3]Jöran Beel:
Towards effective research-paper recommender systems and user modeling based on mind maps. Otto von Guericke University Magdeburg, 2015 - [c27]Jöran Beel, Stefan Langer:
A Comparison of Offline Evaluations, Online Evaluations, and User Studies in the Context of Research-Paper Recommender Systems. TPDL 2015: 153-168 - [c26]Jöran Beel, Stefan Langer, Georgia M. Kapitsaki, Corinna Breitinger, Bela Gipp:
Exploring the Potential of User Modeling Based on Mind Maps. UMAP 2015: 3-17 - 2014
- [j2]Jöran Beel, Stefan Langer, Bela Gipp, Andreas Nürnberger:
The Architecture and Datasets of Docear's Research Paper Recommender System. D Lib Mag. 20(11/12) (2014) - [c25]Jöran Beel, Stefan Langer, Marcel Genzmehr, Bela Gipp:
Utilizing Mind-Maps for Information Retrieval and User Modelling. UMAP 2014: 301-313 - 2013
- [c24]Jöran Beel, Stefan Langer, Marcel Genzmehr, Andreas Nürnberger:
Persistence in Recommender Systems: Giving the Same Recommendations to the Same Users Multiple Times. TPDL 2013: 386-390 - [c23]Jöran Beel, Stefan Langer, Marcel Genzmehr:
Sponsored vs. Organic (Research Paper) Recommendations and the Impact of Labeling. TPDL 2013: 391-395 - [c22]Jöran Beel, Stefan Langer, Andreas Nürnberger, Marcel Genzmehr:
The Impact of Demographics (Age and Gender) and Other User-Characteristics on Evaluating Recommender Systems. TPDL 2013: 396-400 - [c21]Mario Lipinski, Kevin Yao, Corinna Breitinger, Jöran Beel, Bela Gipp:
Evaluation of header metadata extraction approaches and tools for scientific PDF documents. JCDL 2013: 385-386 - [c20]Jöran Beel, Stefan Langer, Marcel Genzmehr, Christoph Müller:
Docear's PDF inspector: title extraction from PDF files. JCDL 2013: 443-444 - [c19]Jöran Beel, Marcel Genzmehr, Stefan Langer:
Docear4Word: reference management for microsoft word based on BibTeX and the citation style language (CSL). JCDL 2013: 445-446 - [c18]Jöran Beel, Stefan Langer, Marcel Genzmehr, Andreas Nürnberger:
Introducing Docear's research paper recommender system. JCDL 2013: 459-460 - [c17]Jöran Beel, Marcel Genzmehr, Stefan Langer, Andreas Nürnberger, Bela Gipp:
A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation. RepSys@RecSys 2013: 7-14 - [c16]Jöran Beel, Stefan Langer, Marcel Genzmehr, Bela Gipp, Corinna Breitinger, Andreas Nürnberger:
Research paper recommender system evaluation: a quantitative literature survey. RepSys@RecSys 2013: 15-22 - 2011
- [c15]Jöran Beel, Stefan Langer:
An exploratory analysis of mind maps. ACM Symposium on Document Engineering 2011: 81-84 - [c14]Bela Gipp, Norman Meuschke, Jöran Beel:
Comparative evaluation of text- and citation-based plagiarism detection approaches using guttenplag. JCDL 2011: 255-258 - [c13]Jöran Beel, Bela Gipp, Stefan Langer, Marcel Genzmehr, Erik Wilde, Andreas Nürnberger, Jim Pitman:
Introducing Mr. DLib, : a machine-readable digital library. JCDL 2011: 463-464 - [c12]Jöran Beel, Bela Gipp, Stefan Langer, Marcel Genzmehr:
Docear: an academic literature suite for searching, organizing and creating academic literature. JCDL 2011: 465-466 - 2010
- [j1]Jöran Beel:
Retrieving Data from Mind Maps to Enhance Search Applications. Bull. IEEE Tech. Comm. Digit. Libr. 6(2) (2010) - [c11]Jöran Beel, Bela Gipp, Ammar Shaker, Nick Friedrich:
SciPlore Xtract: Extracting Titles from Scientific PDF Documents by Analyzing Style Information (Font Size). ECDL 2010: 413-416 - [c10]Bela Gipp, Adriana Taylor, Jöran Beel:
Link Proximity Analysis - Clustering Websites by Examining Link Proximity. ECDL 2010: 449-452 - [c9]Bela Gipp, Jöran Beel:
Citation based plagiarism detection: a new approach to identify plagiarized work language independently. HT 2010: 273-274 - [c8]Jöran Beel, Bela Gipp:
On the robustness of google scholar against spam. HT 2010: 297-298 - [c7]Jöran Beel, Bela Gipp:
Enhancing search applications by utilizing mind maps. HT 2010: 303-304 - [c6]Jöran Beel, Bela Gipp:
Link analysis in mind maps: a new approach to determining document relatedness. ICUIMC 2010: 38
2000 – 2009
- 2009
- [c5]Jöran Beel, Bela Gipp, Jan-Olaf Stiller:
Information retrieval on mind maps - what could it be good for? CollaborateCom 2009: 1-4 - [c4]Jöran Beel, Bela Gipp:
Google Scholar's Ranking Algorithm: The Impact of Articles' Age (An Empirical Study). ITNG 2009: 160-164 - [c3]Jöran Beel, Bela Gipp:
Google Scholar's Ranking Algorithm: The Impact of Citation Counts (An Empirical Study). RCIS 2009: 439-446 - 2008
- [c2]Jöran Beel, Bela Gipp:
The Potential of Collaborative Document Evaluation for Science. ICADL 2008: 375-378 - 2007
- [b2]Bela Gipp, Jöran Beel, Ivo Rössling:
ePassport - The World's New Electronic Passport: A Report about the ePassport's Benefits, Risks and its Security. CreateSpace Independent Publishing Platform 2007, ISBN 978-1-434-82318-2, pp. I-XIV, 1-88 - 2005
- [b1]Jöran Beel, Bela Gipp:
ePass - der neue biometrische Reisepass: Eine Analyse der Datensicherheit, des Datenschutzes sowie der Chancen und Risiken. Shaker 2005, ISBN 978-3-8322-4693-8, pp. 1-115 - 2003
- [c1]Felix Alcalá, Jöran Beel, Arne Frenkel, Bela Gipp, Johannes Lülf, Hagen Höpfner:
Ortung von mobilen Geräten für die Realisierung lokationsbasierter Diensten. Mobilität und Informationssysteme 2003
Coauthor Index
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