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Dr. Joeran Beel

Ussher Assistant Professor (Computer Science)

 


Please visit https://www.scss.tcd.ie/joeran.beel/ for more details
  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
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, 2015, Thesis, PUBLISHED

  

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