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Dr. Joeran Beel

Ussher Assistant Professor (Computer Science)

 


Joeran Beel is Assistant Professor in Intelligent Systems at Trinity College Dublin and a member of the ADAPT Centre. His research focuses on recommender systems (recommendations as-a-service and recommender-system evaluation) and related technologies such as machine learning and natural language processing. Joeran is further interested in the blockchain/cryptocurrencies, plagiarism detection, and information extraction in the fields of digital libraries, finance, tourism, transport, and healthcare. Joeran has published three books and over 50 peer-reviewed articles and has been awarded various grants for research projects, patent applications, and prototype development as well as some business start-up funding. He is involved in the development of open-source projects such as Mr. DLib, Docear, JabRef, and Freeplane, some of which he initiated. Joeran founded two successful IT start-ups and received multiple awards and prizes for each. Joeran studied and researched in the USA (Berkeley), Australia (Sydney), Germany (Magdeburg & Konstanz), Cyprus (Nicosia) and England (Lancaster). He has an M.Sc. in Project Management, an M.Sc. in Business Information Systems and a PhD in Computer Science. Prior to Trinity College, Joeran worked as IT product manager in the tourism industry (Munich, Germany), and as a postdoctoral researcher at the National Institute of Informatics in Tokyo, Japan.
  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
 DISCANT: Domain-Independent Semantic Annotation of the Text
 Mr. DLib
 Docear
 CitePlag
 OriginStamp

Language Skill Reading Skill Writing Skill Speaking
English Fluent Fluent Fluent
German Fluent Fluent Fluent
Joeran Beel, Please visit my Google Scholar profile for a complete list of publications, https://scholar.google.de/citations?user=jyXACVcAAAAJ&hl=en, 2018, Journal Article, SUBMITTED
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, Conference Paper, PUBLISHED
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, Conference Paper, PUBLISHED
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, Conference Paper, PUBLISHED
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, Conference Paper, PUBLISHED
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, Conference Paper, PUBLISHED
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, Conference Paper, PUBLISHED
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, Conference Paper, PUBLISHED
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, Conference Paper, PUBLISHED
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, Conference Paper, PUBLISHED
  

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Joeran Beel, Virtual Citation Proximity (VCP): Calculating Co-Citation-Proximity-Based Document Relatedness for Uncited Documents with Machine Learning, 2017, Notes: [The relatedness of research articles, patents, legal documents, web pages, and other documents is often calculated with citation or hyperlink based approaches such as citation proximity analysis (CPA). In contrast to text-based document similarity, citation-based relatedness covers a broader range of relatedness. However, citation-based approaches suffer from the many documents that receive little or no citations, and for which document relatedness hence cannot be calculated. I propose to calculate a machine-learned 'virtual citation proximity' (or 'virtual hyperlink proximity') that could be calculated for all documents for which textual information (title, abstract ) and metadata (authors, journal name ) is available. The input to the machine learning algorithm would be a large corpus of documents, for which textual information, metadata and citation proximity is available. The citation proximity would serve as ground truth, and the machine-learning algorithm would infer, which textual features correspond to a high proximity of co-citations. After the training phase, the machine-learning algorithm could calculate a virtual citation proximity even for uncited documents. This virtual citation proximity would express in what proximity two documents would likely be cited, if they were cited. The virtual citation proximity then could be used in the same way as "real" citation proximity to calculate document relatedness, and would potentially cover a wider range of relatedness than text-based document relatedness.], Working Paper, PUBLISHED
Joeran Beel, Towards Effective Research-Paper Recommender Systems and User Modeling based on Mind Maps, PhD Thesis. Otto-von-Guericke Universit{\"a, 2015, Journal Article, PUBLISHED

  

Award Date
DAAD Postdoctoral Fellowship (FIT Weltweit) 2015
5th prize in B-P-W business plan contest 2013
1st prize in ego.BUSINESS business plan contest 2012
Best graduate of the Computer Science Department 2007/08 2008
2nd prize in business plan contest 'B-P-W' 2003
2nd winner at "Jugend-forscht", Germany's most reputable research contest for youth (national wide round) 2002
Award for an outstanding microelectronic equipment development by the Association of German Electrical Engineers 2001
Recommender Systems, User Modelling, Information Retrieval, Machine Learning, Artificial Intelligence, Information Extraction, Natural Language Processing, Text Mining, Citation Analysis, Bibliometrics, Altmetrics, Scientometrics, Plagiarism Detection, Blockchain, Digital Libraries, Digital Humanities, Finance (FinTech), Legal, Tourism, Medical