Blog

Recommender Systems

Mr. DLib’s Living Lab for Scholarly Recommendations (preprint)

We published a manuscript on arXiv about the first living lab for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for scholarly recommendations, i.e., research papers, citations, conferences, research grants etc. Recommendations are delivered through the living lab´s API in platforms such as reference management software and digital libraries. The living lab is built on top of the recommender system as-a-service Mr. DLib. Current partners are the reference management software JabRef and the CORE research team. We present the architecture of Mr. DLib’s living lab as well as usage statistics on the first ten months of operating it. During this time, 970,517 recommendations were delivered with a mean click-through rate of 0.22%. Read more…

Recommender Systems

RARD II: The 2nd Related-Article Recommendation Dataset (preprint)

We released a new version of RARD, i.e. RARD II and describe the new release in a preprint published on arXiv. The dataset is available at http://data.mr-dlib.org and the new manuscript is available on arXiv and here in our Blog. The main contribution of this paper is to introduce and describe a new recommender-systems dataset (RARD II). It is based on data from a recommender-system in the digital library and reference management software domain. As such, it complements datasets from other domains such as books, movies, and music. The RARD II dataset encompasses 89m recommendations, covering an item-space of 24m unique items. RARD II provides a range of rich recommendation data, beyond conventional ratings. For example, in addition to the usual rating Read more…

Partnerships

2-week visit at the NII Tokyo: Dagstuhl-Seminar Presentation, Recommender-System and Machine-Learning Research, …

From the 19th of June until the 2nd of July 2018, I am in Japan at the National Institute of Informatics (NII). It is my first visit as Visiting Professor at the NII, and I am working with Professor Akiko Aizawa on a recommender-system and machine-learning project. Besides that work, there are many interesting presentations and other visitors at the NII. Among others, Prof. Dr. Raimund Seidel is visiting. Prof. Seidel is the organizer of the prestigious Dagstuhl seminar. Dagstuhl enables leading researchers in computer science meet for a few days to discuss the latest advancements and challenges in their fields. The NII is organizing a similar seminar series, the Shonan seminars, and Prof. Seidel shared his experience with organizing the Dagstuhl in a Read more…

Partnerships

MoU between the National Institute of Informatics (NII) and Trinity College Dublin (TCD) / SCSS / ADAPT

After being appointed as a visiting professor at the National Institute of Informatics (NII) a few weeks ago, there is more good news. The NII has signed a memorandum of understanding (MoU) with the School of Computer Science and Statistics (SCSS) of Trinity College Dublin (TCD) and the ADAPT Research Centre. The MoU originates from our long-term collaboration with the NII and outlines a partnership between TCD/SCSS/ADAPT and the NII. The goal of the partnership is to: Engage in joint efforts in research Work together in the area of institute management Exchange administrative and managerial staff Collaborate with industry and agencies for public administration and public services Exchange of research and academic staff and students We begin the collaboration with two projects. First, PhD Read more…

Machine Learning

Aiur by Iris.ai: Solving the Problems of Science via the Blockchain and Artificial Intelligence

I am on the advisory board of Iris.ai, a business start-up in the field of open science, blockchain, and artificial intelligence. Iris.ai was founded in 2015 and since then has released several exciting products that have the potential to revolutionize how scientists work. With their latest project, Project Aiur, the team behind Iris.ai envisions a world where the right scientific knowledge is available at our fingertips; where all research is validated and reproducible; where interdisciplinary connections are the norm; where unbiased scientific information flows freely; where research already paid for with our tax money is freely accessible to all. To realize Project Aiur, Iris.ai sells “AIUR tokens”, a new currency that could be the central currency in a new world of Read more…

Machine Learning

DAAD Artificial Intelligence / Machine Learning Tour Through Germany for Postdocs from Outside Germany

The German Academic Exchange Service (DAAD) organizes the “Postdoctoral Researchers’ Networking AI Tour” 2018 to offer on-site visits to universities, research institutes and companies in the field of artificial intelligence, discussions with experts and numerous networking opportunities. Programme-related costs and a travel allowance are covered by DAAD. We have cooperated and received funding from the DAAD on numerous occasions, e.g. to participate in conferences, receive interns, or conduct research visits abroad e.g. in Cyprus and Tokyo. We were always pleased by the professional organization of DAAD events and great networking opportunities that DAAD offered. As such, we can highly recommend applying for the DAAD Artificial Intelligence Tour through Germany, if you are a postdoc from outside Germany who considers working and living Read more…

Trinity College Dublin (Ireland)

Trinity College Dublin announces a €25 million donation from Naughton Family paving the way for a new €60 million E3 Institute

Trinity College Dublin announced plans for a €60 million new E3 Institute in Engineering, Energy and Environment that has been made possible with a major private philanthropic donation by the Naughton family through the Naughton Foundation, established by the founder of the Glen Dimplex Group, Dr Martin Naughton, and his wife, Carmel. This will be combined with Government funding from the Department of Education and Skills. The Naughton family has made the single largest private philanthropic donation in the history of the state to the new E3 institute by donating €25 million. An additional €15 million is being made available by the Department of Education and Skills. This funding will be provided through the Higher Education Authority (HEA). In addition Read more…

Information Extraction

Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parsers

Our paper “Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parsers” got recently accepted and will be presented at Joint Conference on Digital Libraries 2018. Abstract: Bibliographic reference parsing refers to extracting machine-readable metadata, such as the names of the authors, the title, or journal name, from bibliographic reference strings. Many approaches to this problem have been proposed so far, including regular expressions, knowledge bases and supervised machine learning. Many open source reference parsers based on various algorithms are also available. In this paper, we apply, evaluate and compare ten reference parsing tools in a specific business use case. The tools are Anystyle-Parser, Biblio, CERMINE, Citation, Citation-Parser, GROBID, ParsCit, PDFSSA4MET, Reference Tagger Read more…

Information Extraction

Who Did What? Identifying Author Contributions in Biomedical Publications using Naïve Bayes

Our paper “Who Did What? Identifying Author Contributions in Biomedical Publications using Naïve Bayes” got recently accepted and will be presented at Joint Conference on Digital Libraries 2018. Abstract: Creating scientific publications is a complex process. It is composed of a number of different activities, such as designing the experiments, analyzing the data, and writing the manuscript. Information about the contributions of individual authors of a paper is important for assessing authors’ scientific achievements. Some biomedical publications contain a short section written in natural language, which describes the roles each author played in the process of preparing the article. In this paper, we present a study of authors’ roles commonly appearing in these sections, and propose an algorithm for automatic Read more…

Machine Learning

Call for Marie Curie Individual Fellowships: We are open to supervise projects relating to recommender-systems, machine learning, and NLP here at TCD Dublin

The European Union has published the call for Individual Marie Curie Fellowships (MSCA) with the application deadline being 12 September 2018. The goal of the Individual Fellowships is to enhance the creative and innovative potential of experienced researchers. Our group has already one Marie Curie fellow, i.e. a postdoctoral researcher, as part of the EU/SFI EDGE fellowship programme. However, we are open to supervise more postdoctoral researchers. If you are interested in applying for the Individual Marie Curie Fellowship and need a supervisor, please contact us. We are particularly interested in projects relating to machine learning (machine translation, machine-learning evaluation, novel machine-learning algorithms, curriculum learning), recommender systems, and natural language processing.

Recommender Systems

Our website ranks #1 for ‘recommender systems ireland’ and ‘recommender systems dublin’ searches on Google

We started working at Trinity College Dublin 1.5 years ago and launched our new website only 2 months ago. Yet, Google ranks our website #1 for the search queries ‘recommender systems ireland‘ and ‘recommender systems dublin‘ and, not surprisingly, for the variations ‘ireland recommender systems‘ and ‘dublin recommender systems‘. Of course, this is not to mean that we, the School of Computer Science and Statistics at Trinity College Dublin or the ADAPT Centre are the undisputed authorities in the field of recommender systems in Dublin or Ireland. There are several notable researchers and institutions more including Prof. Barry Smyth, Dr Derek Bridge and the Insight Centre. However, this good Google ranking is a flattering approval of our work in the field of recommender systems. For more details on our work please Read more…

Jobs & Internships

We are hiring (again): Software Engineer / Machine-Learning Engineer / Software Architect / Product Manager for a Recommender-Systems Spin-Out Company (TCD Dublin, Ireland)

We have received funding to hire two employees to spin-out a business start-up in the field of recommendations-as-a-service. The two positions are to be filled with two machine-learning engineers, software engineers, software architects or product managers and both employees are expected to work together very closely here at Trinity College Dublin, the ADAPT Centre respectively. The employees will be responsible for developing a recommender-system as-a-service that uses a unique machine-learning technology, which is based on the research of Asst. Professor Joeran Beel who is the project lead. The first position is already filled with a full-stack software engineer and software architect. This person is flexible in the responsibilities and open to focus more on either the software engineering / machine-learning Read more…