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Joeran Beel
Ussher Assistant Professor, Computer Science

Biography

Dr Joeran Beel is a tenure-track Ussher Assistant Professor in Intelligent Systems at the School of Computer Science and Statistics at Trinity College Dublin. He is also affiliated with the ADAPT Centre, an interdisciplinary research centre that closely cooperates with industry partners including Google, Deutsche Bank, Huawei, and Novartis. Joeran is further a Visiting Professor at the National Institute of Informatics in Tokyo, where he also completed his postdoctoral research. Joeran holds two Master's degrees and obtained a PhD in computer science from Otto-von-Guericke University, Magdeburg (Germany). During his PhD studies, Joeran completed three research visits at the University of California, Berkeley and one at the University of Cyprus. Joeran has industry experience as a product manager, as a freelance consultant and as the founder of two business start-ups, which both received several awards at business plan competitions such as start2grow and BPW. Currently, Joeran is spinning out his third business start-up.

Joeran's research focuses on automated machine learning (AutoML) & meta-learning, information retrieval (IR), natural language processing (NLP), the blockchain and other technologies, in areas including recommender systems, algorithm selection, news analysis, plagiarism detection, and document engineering. Domains he is particularly interested in include digital libraries & digital humanities, eHealth, tourism, law, FinTech, and mobility.

Joeran published more than 70 peer-reviewed publications that have received over 2,000 citations. He acts as a reviewer for venues such as SIGIR, ECIR, RecSys, UMAP, ACM TiiS, and JASIST and served as general co-chair of the 26th Irish Conference on Artificial Intelligence and Cognitive Science. Joeran acquired more than 2 million Euro in funding for his research, prototype development, and business start-ups.

Publications and Further Research Outputs

Peer-Reviewed Publications

Philipp Scharpf, Ian Mackerracher, Moritz Schubotz, Joeran Beel, Corinna Breitinger, Bela Gipp, AnnoMathTeX - a Formula Identifer Annotation Recommender System for Mathematical Documents, ACM Recommender Systems Conference, 2019, pp532-533 Conference Paper, 2019

Mark Grennan, Martin Schibel, Andrew Collins, Joeran Beel, GIANT: The 1-Billion Annotated Bibliographic-Reference-String Dataset for Deep Citation Parsing, 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS'19), 2019, pp18-30 Conference Paper, 2019

Keith Tunstead, Joeran Beel, Combating Stagnation in Reinforcement Learning Through 'Guided Learning' With 'Taught-Response Memory', 3rd International Tutorial & Workshop on Interactive Adaptive Learning (IAL2019) at the ECML PKDD Conference, 2019, pp96-103 Conference Paper, 2019

Hebatallah A. Mohamed Hassan, Giuseppe Sansonetti, Fabio Gasparetti, Alessandro Micarelli, Joeran Beel, BERT, ELMo, USE and InferSent Sentence Encoders: The Panacea for Research-Paper Recommendation?, 13th ACM Conference on Recommender Systems (RecSys), 2019, pp6-10 Conference Paper, 2019

Dominika Tkaczyk, Andrew Collins, Joeran Beel, NaïveRole: Author-Contribution Extraction and Parsing from Biomedical Manuscripts, 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS'19), 2019, pp50-62 Conference Paper, 2019

Nicholas Bonello, Jeremy Debattista, Joeran Beel, Seamus Lawless, Multi-stream Data Analytics for Enhanced Performance Prediction in Fantasy Football, 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS'19), 2019, pp94-105 Conference Paper, 2019

Felix Beierle, Akiko Aizawa, Andrew Collins, and Joeran Beel, Choice Overload in Research-Paper Recommender Systems, International Journal of Digital Libraries, 2019, p12 - 38 Journal Article, 2019

Conor O'Sullivan, Joeran Beel, Predicting the Outcome of Judicial Decisions made by the European Court of Human Rights, 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS'19), 2019, pp101-112 Conference Paper, 2019

Joeran Beel, Alan Griffin, Conor O'Shea, Darwin & Goliath: Recommendations-As-a-Service (RaaS) with Automated Algorithm-Selection and White-Labels, ACM Recommender Systems Conference, 2019, pp534-535 Conference Paper, 2019

Andrew Collins, Joeran Beel, A First Analysis of Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems, Workshop on Recommendation in Complex Scenarios (ComplexRecs), 13th ACM Conference on Recommender Systems (RecSys 2019), 2019, pp29-34 Conference Paper, 2019

Collins, Andrew, and Joeran Beel, Keyphrases vs. Document Embeddings vs. Terms for Recommender Systems: An Online Evaluation, ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), 2019, pp110 - 115 Conference Paper, 2019

Joeran Beel, Victor Brunel, Data Pruning in Recommender Systems Research: Best-Practice or Malpractice?, 13th ACM Conference on Recommender Systems (RecSys), 2019, pp26-30 Conference Paper, 2019

Edenhofer, Gordian, Andrew Collins, Akiko Aizawa, and Joeran Beel, Augmenting the DonorsChoose.org Corpus for Meta-Learning, 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR), 2019, pp32 - 38 Conference Paper, 2019

J Beel, B Smyth, A Collins, RARD II: The 94 Million Related-Article Recommendation Dataset, 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR), 2019, pp39 - 55 Conference Paper, 2019

J Beel, A Collins, O Kopp, L Dietz, P Knoth, Online Evaluations for Everyone: Mr. DLib's Living Lab for Scholarly Recommendations, 41st European Conference on Information Retrieval (ECIR), 2019, pp100 - 103 Conference Paper, 2019

Beel, Joeran, and Lars Kotthoff, Proposal for the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR), 41st European Conference on Information Retrieval (ECIR), 2019, pp205-212- Conference Paper, 2019

Collier M. and Beel J., Memory-Augmented Neural Networks for Machine Translation, Machine Translation (MT) Summit, 2019, pp30 - 42 Conference Paper, 2019

Dominika Tkaczyk, Andrew Collins and Joeran Beel, Who Did What? Identifying Author Contributions in Biomedical Publications using Naïve Bayes, ACM Joint Conference on Digital Libraries (JCDL), 2018, pp387-388 Conference Paper, 2018 DOI

Dominika Tkaczyk, Andrew Collins, Paraic Sheridan and Joeran Beel, Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parsers, ACM Joint Conference on Digital Libraries (JCDL), 2018, pp99-108 Conference Paper, 2018 DOI

Dominika Tkaczyk, Rohit Gupta, Riccardo Cinti and Joeran Beel, ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers, 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS), 2018, pp162 - 173 Conference Paper, 2018

Dominika Tkaczyk, Paraic Sheridan, Joeran Beel, ParsRec: A Meta-Learning Recommender System for Bibliographic Reference Parsing Tools, 12th ACM Conference on Recommender Systems, 2018, pp101 - 102 Conference Paper, 2018

Mark Collier and Joeran Beel, Implementing Neural Turing Machines, International Conference on Artificial Neural Networks, 2018-10-05, 2018, pp10-18 Conference Paper, 2018 DOI

Andrew Collins, Dominika Tkaczyk, Akiko Aizawa, and Joeran Beel, Position Bias in Recommender Systems for Digital Libraries, iConference , 2018, pp335-344 Conference Paper, 2018 DOI

Andrew Collins, Dominika Tkaczyk, and Joeran Beel, A Novel Approach to Recommendation Algorithm Selection using Meta-Learning, 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS), 2018, pp162 - 173 Conference Paper, 2018

Joeran Beel, Andrew Collins, and Akiko Aizawa, The Architecture of Mr. DLib's Scientific Recommender-System API, 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS), Dublin, 2018, pp78 - 89 Conference Paper, 2018

Mark Collier and Joeran Beel, An Empirical Comparison of Syllabuses for Curriculum Learning, 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS), 2018, pp150 - 161 Conference Paper, 2018

Joeran Beel, Stefan Langer, and Bela Gipp, TF-IDuF: A Novel Term-Weighting Scheme for User Modeling based on Users' Personal Document Collections, Proceedings of the 12th iConference, 2017, pp34-40 Conference Paper, 2017

Bela Gipp, Norman Meuschke, Joeran Beel, and Corinna Breitinger, Using the Blockchain of Cryptocurrencies for Timestamping Digital Cultural Heritage, Bulletin of IEEE Technical Committee on Digital Libraries (TCDL), 2017, p12-14 Journal Article, 2017

Joeran Beel, Zeljko Carevic, Johann Schaible, and Gabor Neusch, RARD: The Related-Article Recommendation Dataset, D-Lib Magazine, 2017, p1-14 Journal Article, 2017 DOI

Stefan Langer and Joeran Beel, Apache Lucene as Content-Based-Filtering Recommender System: 3 Lessons Learned, 5th International Workshop on Bibliometric-enhanced Information Retrieval (BIR) at the 39th European Conference on Information Retrieval (ECIR), 2017, pp85-92 Conference Paper, 2017

Joeran Beel, Siddharth Dinesh, Philipp Mayr, Zeljko Carevic, and Jain Raghvendra, Stereotype and Most-Popular Recommendations in the Digital Library Sowiport, Proceedings of the 15th International Symposium of Information Science, 2017, pp96-108 Conference Paper, 2017

Felix Beierle, Akiko Aizawa, and Joeran Beel, Exploring Choice Overload in Related-Article Recommendations in Digital Libraries, 5th International Workshop on Bibliometric-enhanced Information Retrieval (BIR) at the 39th European Conference on Information Retrieval (ECIR), 2017, pp51-61 Conference Paper, 2017 URL URN

Stefan Feyer, Sophie Siebert, Bela Gipp, Akiko Aizawa, and Joeran Beel, Integration of the Scientific Recommender System Mr. DLib into the Reference Manager JabRef, Proceedings of the 39th European Conference on Information Retrieval (ECIR), 2017, pp770-774 Conference Paper, 2017 DOI

Joeran Beel, Bela Gipp, and Akiko Aizawa, Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia, Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), 2017, pp313-314 Conference Paper, 2017 URL DOI

Joeran Beel, Real-World Recommender Systems for Academia: The Gain and Pain in Developing, Operating, and Researching them, 5th International Workshop on Bibliometric-enhanced Information Retrieval (BIR) at the 39th European Conference on Information Retrieval (ECIR), 2017, pp6-17 Conference Paper, 2017 URL URN

Bela Gipp, Corinna Breitinger, Norman Meuschke, and Joeran Beel, CryptSubmit: Introducing Securely Timestamped Manuscript Submission and Peer Review Feedback using the Blockchain, Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), 2017, pp1-4 Conference Paper, 2017 DOI

Joeran Beel, Corinna Breitinger, and Stefan Langer, Evaluating the CC-IDF citation-weighting scheme: How effectively can "Inverse Document Frequency" (IDF) be applied to references, Proceedings of the 12th iConference, 2017, pp1-11 Conference Paper, 2017

Weiler, Andreas and Beel, Joeran and Gipp, Bela and Grossniklaus, Michael, Stability Evaluation of Event Detection Techniques for Twitter, Advances in Intelligent Data Analysis XV, edited by Bostrm, Henrik and Knobbe, Arno and Soares, Carlos and Papapetrou, Panagiotis , Springer, 2016, pp368--380 Conference Paper, 2016 DOI

Joeran Beel and Bela Gipp and Stefan Langer and Corinna Breitinger, Research Paper Recommender Systems: A Literature Survey, International Journal on Digital Libraries, (4), 2016, p305-338 Journal Article, 2016 DOI

Beel, Joeran and Breitinger, Corinna and Langer, Stefan and Lommatzsch, Andreas and Gipp, Bela, Towards Reproducibility in Recommender-Systems Research, User Modeling and User-Adapted Interaction (UMUAI), 26, (1), 2016, p69-101 Journal Article, 2016 DOI

Beel, Joeran and Langer, Stefan and Kapitsaki, Georgia M. and Breitinger, Corinna and Gipp, Bela, Exploring the Potential of User Modeling based on Mind Maps, Proceedings of the 23rd Conference on User Modelling, Adaptation and Personalization (UMAP), edited by Francesco Ricci and Kalina Bontcheva and Owen Conlan and Séamus Lawless , 9146, Springer, 2015, pp3-17 Conference Paper, 2015 DOI

Joeran Beel and Stefan Langer, A Comparison of Offline Evaluations, Online Evaluations, and User Studies in the Context of Research-Paper Recommender Systems, Proceedings of the 19th International Conference on Theory and Practice of Digital Libraries (TPDL), edited by Kapidakis, Sarantos and Mazurek, Cezary and Werla, Marcin , 9316, 2015, pp153-168 Conference Paper, 2015 DOI

Joeran Beel and Stefan Langer and Bela Gipp and Andreas Nuernberger, The Architecture and Datasets of Docear's Research Paper Recommender System, D-Lib Magazine, 20, (11/12), 2014 Journal Article, 2014 DOI

Joeran Beel, Utilizing Mind-Maps for Information Retrieval and User Modelling, Proceedings of the 22nd Conference on User Modelling, Adaption, and Personalization (UMAP), edited by Vania Dimitrova and Tsvi Kuflik and David Chin and Francesco Ricci and Peter Dolog and Geert-Jan Houben , 8538, Springer, 2014, pp301-313 Conference Paper, 2014 DOI

Stefan Langer and Joeran Beel, The Comparability of Recommender System Evaluations and Characteristics of Docear's Users, Proceedings of the Workshop on Recommender Systems Evaluation: Dimensions and Design (REDD) at the 2014 ACM Conference Series on Recommender Systems (RecSys), CEUR-WS, 2014, pp1--6 Conference Paper, 2014

Joeran Beel and Stefan Langer and Marcel Genzmehr and Christoph MÃ"ller, Docears PDF Inspector: Title Extraction from PDF files, Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL'13), ACM, 2013, pp443-444 Conference Paper, 2013 DOI

Joeran Beel and Stefan Langer and Marcel Genzmehr and Bela Gipp and Corinna Breitinger and Andreas Nürnberger, Research Paper Recommender System Evaluation: A Quantitative Literature Survey, Proceedings of the Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys) at the ACM Recommender System Conference (RecSys), ACM, 2013, pp15-22 Conference Paper, 2013 DOI

Joeran Beel and Stefan Langer and Marcel Genzmehr, Docear4Word: Reference Management for Microsoft Word based on BibTeX and the Citation Style Language (CSL), Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL'13), ACM, 2013, pp445-446 Conference Paper, 2013 DOI

Joeran Beel and Stefan Langer and Marcel Genzmehr and Andreas NÃ"rnberger, Persistence in Recommender Systems: Giving the Same Recommendations to the Same Users Multiple Times, Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013), Valletta, Malta, edited by Trond Aalberg and Milena Dobreva and Christos Papatheodorou and Giannis Tsakonas and Charles Farrugia , 8092, Springer, 2013, pp390--394 Conference Paper, 2013

Joeran Beel and Stefan Langer and Andreas Nuernberger and Marcel Genzmehr, The Impact of Demographics (Age and Gender) and Other User Characteristics on Evaluating Recommender Systems, Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013), Valletta, Malta, edited by Trond Aalberg and Milena Dobreva and Christos Papatheodorou and Giannis Tsakonas and Charles Farrugia , Springer, 2013, pp400--404 Conference Paper, 2013

Beel, Joeran and Langer, Stefan and Genzmehr, Marcel, Sponsored vs. Organic (Research Paper) Recommendations and the Impact of Labeling, Proceedings of the 17th International Conference on Theory and Practice of Digital Libraries (TPDL 2013), Valletta, Malta, edited by Trond Aalberg and Milena Dobreva and Christos Papatheodorou and Giannis Tsakonas and Charles Farrugia , 2013, pp395--399 Conference Paper, 2013

Joeran Beel and Stefan Langer and Marcel Genzmehr and Andreas NÃŒrnberger, Introducing Docear's Research Paper Recommender System, Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL'13), ACM, 2013, pp459-460 Conference Paper, 2013 DOI

Joeran Beel and Stefan Langer and Marcel Genzmehr and Bela Gipp and Andreas NÃ"rnberger, A Comparative Analysis of Offline and Online Evaluations and Discussion of Research Paper Recommender System Evaluation, Proceedings of the Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys) at the ACM Recommender System Conference (RecSys), 2013, pp7-14 Conference Paper, 2013 DOI

Mario Lipinski and Kevin Yao and Corinna Breitinger and Joeran Beel and Bela Gipp, Evaluation of Header Metadata Extraction Approaches and Tools for Scientific PDF Documents, Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries (JCDL'13), Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries (JCDL'13), 2013, pp385-386 Conference Paper, 2013

Joeran Beel, Bela Gipp, Stefan Langer, Marcel Genzmehr, Erik Wilde, Andreas Nürnberger, Jim Pitman, Docear: An academic literature suite for searching, organizing and creating academic literature, 11th annual international ACM/IEEE joint conference on Digital libraries (JCDL), 2011, pp465-466 Conference Paper, 2011

Joeran Beel, Bela Gipp, Stefan Langer, Marcel Genzmehr, Erik Wilde, Andreas Nürnberger, Jim Pitman, Introducing Mr. DLib, a machine-readable digital library, 11th annual international ACM/IEEE joint conference on Digital libraries (JCDL), 2011, pp463-464 Conference Paper, 2011

Bela Gipp, Norman Meuschke, Joeran Beel, Comparative evaluation of text-and citation-based plagiarism detection approaches using guttenplag, 11th annual international ACM/IEEE joint conference on Digital libraries (JCDL), 2011, pp255-258 Conference Paper, 2011

Beel, Joeran and Gipp, Bela, Academic search engine spam and Google Scholar's resilience against it, Journal of Electronic Publishing, 13, (3), 2010 Journal Article, 2010 DOI

Bela Gipp, Adriana Taylor, Jöran Beel, Link Proximity Analysis - Clustering Websites by Examining Link Proximity, International Conference on Theory and Practice of Digital Libraries (TPDL), 2010, pp449--452 Conference Paper, 2010

Jöran Beel, Bela Gipp, Enhancing Information Search by Utilizing Mind Maps, 21st ACM Conference on Hypertext and Hypermedia, 2010, pp303-304 Conference Paper, 2010

Bela Gipp, Joeran Beel, Citation based plagiarism detection: a new approach to identify plagiarized work language independently, 21st ACM Conference on Hypertext and hypermedia, 2010, pp273-274 Conference Paper, 2010

Joeran Beel, Bela Gipp, Ammar Shaker, Nick Friedrich, SciPlore Xtract: extracting titles from scientific PDF documents by analyzing style information (font size), International Conference on Theory and Practice of Digital Libraries (TPDL), 2010, pp413-416 Conference Paper, 2010

Joeran Beel, Bela Gipp, On the robustness of Google Scholar against spam, 21st ACM Conference on Hypertext and Hypermedia, 2010, pp297-298 Conference Paper, 2010

Jöran Beel, Bela Gipp, Link analysis in mind maps: a new approach to determining document relatedness, 4th International Conference on Uniquitous Information Management and Communication, 2010, pp38-42 Conference Paper, 2010

Joeran Beel, Bela Gipp, Google Scholar's ranking algorithm: The impact of citation counts (An empirical study), Third International Conference on Research Challenges in Information Science, 2009, pp439--446 Conference Paper, 2009

Bela Gipp, Jöran Beel, Citation proximity analysis (CPA): A new approach for identifying related work based on co-citation analysis, 12th International Conference on Scientometrics and Informetrics, 2009, pp571 - 575 Conference Paper, 2009

Jöran Beel, Bela Gipp, Erik Wilde, Academic Search Engine Optimization (ASEO): Optimizing Scholarly Literature for Google Scholar & Co., Journal of scholarly publishing , 41, (2), 2009, p176-190 Journal Article, 2009

Bela Gipp, Jöran Beel, Christian Hentschel, Scienstein: A research paper recommender system, International conference on emerging trends in computing, 2009, pp309 - 315 Conference Paper, 2009

Jöran Beel, Bela Gipp, Google Scholar's ranking algorithm: an introductory overview, 12th International Conference on Scientometrics and Informetrics (ISSI'09), 2009, pp230-241 Conference Paper, 2009

Bela Gipp, Jöran Beel, Identifying related documents for research paper recommender by CPA and COA, Proceedings of the World Congress on Engineering and Computer Science, 2009, pp20-22 Conference Paper, 2009

Bela Gipp, Jöran Beel, Google scholar's ranking algorithm: The impact of articles' age (an empirical study), Sixth International Conference on Information Technology: New Generations, 2009, pp160-164 Conference Paper, 2009

Jöran Beel, Bela Gipp, Jan-Olaf Stiller, Information retrieval on mind maps-what could it be good for?, 5th International Conference on Collaborative Computing: Networking, Applications and Worksharing, 2009, pp1-4 Conference Paper, 2009

Jöran Beel, Bela Gipp, The potential of collaborative document evaluation for science, International Conference on Asian Digital Libraries (ICADL), 2008, pp375-378 Conference Paper, 2008

Felix Alcala, Jöran Beel, Arne Frenkel, Bela Gipp, Johannes Lülf, Hagen Höpfner, Ubiloc: A system for locating mobile devices using mobile devices, 1st Workshop on Positioning, Navigation and Communication, 2004, pp43 - 48 Conference Paper, 2004

Felix Alcala, Jöran Beel, Arne Frenkel, Béla Gipp, Johannes Lülf, Hagen Höpfner, Ortung von mobilen Geräten für die Realisierung lokationsbasierter Diensten, Mobilität und Informationssysteme, 2003, pp100 - 104 Conference Paper, 2003

Non-Peer-Reviewed Publications

Joeran Beel, Please visit my Google Scholar profile for a complete list of publications, https://scholar.google.de/citations?user=jyXACVcAAAAJ&hl=en, 2020 Journal Article, 2020

Joeran Beel, Mr DLib: Related-Article Recommendations for Digital Libraries As-a-Service, LibTech / 48th LIBER Annual Conference, Dublin, 2019 Invited Talk, 2019

Joeran Beel, Federated Meta-Learning: Democratizing Algorithm Selection Across Disciplines and Software Libraries, 2019, - Miscellaneous, 2019

Joeran Beel, Meta-Learning for Per-Instance Algorithm Selection, Deep-Learning Meetup, Dublin, 2019 Invited Talk, 2019

Joeran Beel, Docear & Recent Advances in Recommender Systems, Inselklinikum Kolloquium, Bern, Switzerland, 2019 Invited Talk, 2019

Joeran Beel and Lars Kotthoff(ed.), The 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR), 2019, 1-65 p Proceedings of a Conference, 2019

Joeran Beel, Reference Parsing, Author Role Extraction, and Micro Recommendations, NII Weekly Seminar, Media Devision, Tokyo, 2018-03-30, 2018 Invited Talk, 2018

Joeran Beel, Darwin & Goliath: Micro & Macro Recommendations as-a-Service, Machine Learning Meetup Dublin, Dublin, 2018-04, 2018 Invited Talk, 2018

Joeran Beel, The Potential of Meta Recommender-Systems at Macro- and Micro Level, Machine Learning Dublin Meetup, Ireland, Dublin, 2018 Invited Talk, 2018

Rob Brennan, Joeran Beel, Ruth Byrne and Jeremy Debattista, Preface: The 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2018), 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS, 2018, pp1 - 7 Conference Paper, 2018

Joeran Beel, Machine Learning und die Bibliothek - Chancen und Grenzen, 18. BVB-Verbundkonferenz, Weiden, Germany, 2018 Invited Talk, 2018

Joeran Beel, Virtual Citation Proximity (VCP): Calculating Co-Citation-Proximity-Based Document Relatedness for Uncited Documents with Machine Learning, 2017 Working Paper, 2017 TARA - Full Text DOI

Joeran Beel, Keynote: Real-World Recommender Systems for Academia: The Pain and Gain in Building, Operating, and Researching them, BIR Workshop, co-located with ECIR, UK, 2017 Invited Talk, 2017

Joeran Beel, My Life as a PhD Student and Researcher, Research Methods Seminar, Dublin, 2017-10-18, 2017, School of Computer Science, TCD Invited Talk, 2017

Joeran Beel, Real-World Recommender Systems for Academia The Pain and Gain in Building, Operating and Researching them, UCD Recommender System Seminar, Dublin, 2017-11-21, 2017 Invited Talk, 2017

Joeran Beel, Work & Life in Tokyo as a Researcher, DAAD Butterbrot & Bier, Tokyo, 2016-10-24, 2016 Invited Talk, 2016

Joeran Beel, Mr. DLib: Literature Recommendations as a Service, Deutsch-Japanischer Wirtschaftskreis, Tokyo, 2016-10-04, 2016 Invited Talk, 2016

Joeran Beel, Towards Effective Research-Paper Recommender Systems and User Modeling based on Mind Maps, 2015 Thesis, 2015

Jöran Beel, Bela Gipp, Christoph Müller, 'SciPlore MindMapping': A Tool for Creating Mind Maps Combined with PDF and Reference Management, D-Lib Magazine, 15, (11), 2009, p38 - 38 Journal Article, 2009

Bela Gipp and Joeran Beel and Ivo Roessling, ePassport: The World's New Electronic Passport, Scotts Valley (USA), Createspace, 2007 Book, 2007

Joeran Beel, Project Team Rewards: Rewarding and Motivating your Project Team, Create Space LLC, Scotts Valley, 2007, 1 - 99pp Book, 2007

Jöran Beel, Bela Gipp, ePass-der neue biometrische Reisepass: eine Analyse der Datensicherheit, des Datenschutzes sowie der Chancen und Risiken, Shaker, 2005, 1 - 115pp Book, 2005

Research Expertise

Projects

  • Title
    • Darwin & Goliath: Meta-Learning Recommendations As-a-Service
  • Summary
    • Recommender systems like on Amazon generate up to 30% of enterprises' consumption. Consequently, many large enterprises employ hundreds of software engineers to develop recommender systems and make them as effective as possible. Small and medium-sized enterprises (SME) often do not have the skills and resources to develop recommender systems from scratch. Instead, they typically use recommendations-as-a-service (RaaS). RaaS providers operate recommender systems on their servers, and SMEs pay a monthly subscription fee to request recommendations from the RaaS' API. Current RaaS providers offer suboptimal recommendation effectiveness because their systems do not adapt optimally to the characteristics of their SME partners. This is due to three reasons. 1. RaaS providers use only a single recommendation-framework. This limits both the number of potential algorithms to use and the type of data they can process. 2. RaaS providers follow a one-size-fits-all approach and optimise their systems globally, i.e. they use one algorithm for all their SMEs. However, research shows that recommendation algorithms perform significantly different with different SMEs; hence one algorithm for all SMEs will deliver suboptimal effectiveness for at least some of them. 3. RaaS providers do not optimise the presentation of recommendations. However, the presentation of recommendations is important as, for instance, the number of recommendations and the displayed information has a strong impact on how users perceive the quality of recommendations. We create the spin-out company Darwin & Goliath, which will offer a unique and highly adaptable meta-recommendation framework as a recommendations-as-a-service. The proposed solution features three USPs. 1. Darwin & Goliath uses multiple state-of-the-art recommendation frameworks (Apache Mahout, TensorFlow, and others). Each of these frameworks provides dozens of pre-implemented algorithms. Consequently, Darwin & Goliath chooses from a wide range of algorithms when it comes to identifying the best algorithm for each SME. 2. Darwin & Goliath applies a novel technology for algorithm selection, which we call "micro optimization". Not only identifies Darwin & Goliath the best algorithm for each SME, but for each recommendation request. This means that even for the same SME, Darwin & Goliath may use a different algorithm for different users, items, and contexts. 3. Darwin & Goliath automatically finds the most favourable way of presenting recommendations for each SME.
  • Funding Agency
    • Enterprise Ireland
  • Date From
    • 2018-05-30
  • Date To
    • 2020-05-29
  • Title
    • Domain-Independent Semantic Annotation of the Text
  • Summary
    • Domain-Independent Semantic Annotation of the Text (DISCANT): Surrounded by huge and exponentially growing volume of information of various nature, every day we face challenges with keeping track of the latest news in a domain of interest or finding good-quality answers to specific questions. The problem of information overload has been addressed by modern information systems and search engines, however, their capabilities are strongly limited by using unstructured textual formats for exchanging and storing information. Textual documents often contain a lot of concrete facts and quantifiable information, but the communication of these facts and information through human languages renders them inaccessible to machines. This "semantic bottleneck" problem substantially slows down communication and knowledge propagation in the world. In order to equip machines with the ability of effective processing of the text, DISCANT ("Domain-Independent SemantiC ANnotation of the Text") aims at creating a comprehensive framework for semantic annotation of textual documents of arbitrary domains, such as scientific papers, legal documents, customer reviews or clinical trial reports. We will develop approaches, methods and tools for two classes of solutions: an environment for discovering entity and relation types in a given domain and a system for automated semantic annotation of the text. The project proposes a novel approach based on a combination of unsupervised natural language processing and machine learning techniques. DISCANT will advance machine understanding of the text, contributing to the release of important knowledge buried in textual documents, the creation of machine-readable knowledge repositories and more effective solutions for semantic search and personal recommendations. As a consequence, DISCANT will equip the consumers of textual documents with better tools for overcoming data deluge and information overload, enabling them to make better-quality, data-driven decisions.
  • Funding Agency
    • EU/SFI
  • Date From
    • 2017-07
  • Date To
    • 2019-06
  • Title
    • SciPlore
  • Summary
    • See http://sciplore.org
  • Funding Agency
    • ego.START
  • Date From
    • 2012
  • Date To
    • 2013
  • Title
    • Mr. DLib and Recommender System Reproducibility
  • Summary
    • Fellowship to conduct research at Tokyo's National Institute of Informatics (NII) for two years in the field of recommender systems.
  • Funding Agency
    • DAAD (German Academic Exchange Service)
  • Date From
    • 2015
  • Date To
    • 2016
  • Title
    • Docear: Literature Management & Mind-Mapping Based User Modelling
  • Summary
    • Docear is a unique solution to academic literature management, i.e. it helps you organizing, creating, and discovering academic literature. Among others, Docear offers: 1. A single-section user-interface that allows the most comprehensive organization of your literature. With Docear, you can sort documents into categories; you can sort annotations (comments, bookmarks, and highlighted text from PDFs) into categories; you can sort annotations within PDFs; and you can view multiple annotations of multiple documents, in multiple categories - at once. 2. A "literature suite concept" that combines several tools in a single application (pdf management, reference management, mind mapping, ...). This allows you to draft your own papers, assignments, thesis, etc. directly in Docear and copy annotations and references from your collection directly into your draft. 3. A recommender system that helps to discover new literature: Docear recommends papers which are free, in full-text, instantly to download, and tailored to your information needs.
  • Funding Agency
    • German Federal Ministry of Economics
  • Date From
    • 2011
  • Date To
    • 2012
  • Title
    • SciPlore
  • Summary
    • See http://sciplore.org
  • Funding Agency
    • German Federal Ministry of Economics
  • Date From
    • 2011
  • Date To
    • 2012
  • Title
    • Docear: Literature Management
  • Summary
    • Docear is a unique solution to academic literature management, i.e. it helps you organizing, creating, and discovering academic literature. Among others, Docear offers: 1. A single-section user-interface that allows the most comprehensive organization of your literature. With Docear, you can sort documents into categories; you can sort annotations (comments, bookmarks, and highlighted text from PDFs) into categories; you can sort annotations within PDFs; and you can view multiple annotations of multiple documents, in multiple categories - at once. 2. A "literature suite concept" that combines several tools in a single application (pdf management, reference management, mind mapping, ...). This allows you to draft your own papers, assignments, thesis, etc. directly in Docear and copy annotations and references from your collection directly into your draft. 3. A recommender system that helps to discover new literature: Docear recommends papers which are free, in full-text, instantly to download, and tailored to your information needs.
  • Funding Agency
    • ego.START
  • Date From
    • 2012
  • Date To
    • 2014

Keywords

ARTIFICIAL INTELLIGENCE; Bibliometrics; Bitcoin; Blockchain; computer-assisted language learning; Cross-Language Information Retrieval; Digital Humanities; Digital Libraries; FinTech; INFORMATION EXTRACTION; INFORMATION-RETRIEVAL; MACHINE LEARNING; Natural Language Processing; Personalisation; Personalisation and User-Centric Adaptivity; Personalised Information Retrieval; Plagiarism Detection; Recommender Systems; Scientometrics; Text Mining; TOURISM; User Modeling

Recognition

Representations

Scientific advisor on the board of the business-start-up iris.ai 2018

Advisor on the board of the business-startup Originstamp. 2016

Member of the Scientific Advisory Board of the German Academic Exchange Service (DAAD) for the IFI Programme for PostDoctoral Funding in AI. 2019

Editorial Board of the International Journal of Digital Libraries 2019

Awards and Honours

Outstanding PC Member Award at the UMAP Conference 2019

Finalist of the NRDC Business Plan Competition 2019

Best-paper award at ICANN Conf. 2018

Best-Reviewer Nominee at 12th ACM Conference on Recommender Systems 2018

5th prize in B-P-W business plan contest 2013

1st / prize in ego.BUSINESS business plan contest 2012

Best Master's graduate of the computer science department 2008

Award for exceptional achievements in the field of technology by the Heinz and Gisela Friederichs Foundation 2002

2nd winner at Jugend-forscht, Germany's national wide research contest 2002

Honoring for outstanding research work by German's Chancellor Gerhard Schröder 2002

4th prize in nationwide business plan contest start2grow 2003

2nd prize in nationwide business plan contest B-P-W 2003

4th winner of the business plan contest futureSAX in Saxony 2003

Award for an outstanding microelectronic equipment development by the Association of German Electrical Engineers 2001

Memberships

Member of the Association of Computing Machinery (ACM) 2010