Conferences

AICS’2018: We Co-Organize the 26th Irish Conference on Artificial Intelligence and Cognitive Science

We are delighted to announce the 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS’2018), which we will co-organize together with Rob Brennan, Ruth Byrne, Jeremy Debattista, and a renowned program committee. AICS 2018 takes place from December 6 to 7, 2018 at Trinity College Dublin, more precisely in the Long Room Hub. Deadline for submissions is 30th September 2018. There will be three tracks for submissions, namely full papers, NECTAR Papers, and student papers. The call for papers invites papers relating particularly to machine learning, machine translation, neural networks, data mining, cognitive modelling, behaviour epistemology, evolutionary computation, recommender systems, collective intelligence, human learning, and several more. AICS 2018 is sponsored by the ADAPT Research Centre, Trinity Long Room Hub, and Trinity College Dublin. AICS dates Read more…

By Joeran Beel, ago
Machine Learning

Machine Learning Meetup at DogPatch — 26th March

The Machine Learning Meetup in Dublin, takes place on the last Monday every month. This month, the machine learning meetup is hosted by Dogpatch and there will be plenty of exciting presentations about machine learning: Marco Forte (PhD researcher, Sigmedia Group) speaks about foreground estimation in machine learning and motion estimation for video matting in machine learning including deep learning techniques to create more consistent and content-aware results. Procheta Sen (PhD researcher, ADAPT Centre) speaks about cross-session search and tempo-lexical context-driven machine-learned word embeddings. Elias Giacoumidis, (Marie-Curie Fellow, CONNECT) speaks about machine learning optical fiber telecommunications. The next machine learning meetup takes place on 26th March at The Vaults – Dogpatch Labs CHQ Building, Custom House Quay, Dublin. Talks are from 18:30 Read more…

By Joeran Beel, ago
Jobs / Career

We Are Hiring: 1 Software/Machine-Learning Engineer & 1 Software Architect / Product Owner for a Recommender-System Business Start-up

UPDATE: We will soon advertise another position for this start-up. Please come back in a few days. The School of Computer Science and Statistics of Trinity College Dublin and the ADAPT Centre received funding to hire 2 employees for 2 years* to spin-out a business start-up in the field of recommender-systems as-a-service and machine learning in Dublin. The two positions are to be filled with one machine-learning engineer and one software architect/product manager, whereas both employees are expected to work together very closely. They will be responsible for developing a recommender-system as-a-service that uses a unique technology, based on the research of Prof Dr Joeran Beel who will be the project lead (read here for a brief outline of the Read more…

By Joeran Beel, ago
Machine Learning

Mr. DLib v1.1.1 released: minor improvements

On 28th February, we released version 1.1.1 of Mr. DLib’s recommender system with some minor improvements and bug fixes: Improved 404 error handling for unknown document IDs Fix: The order of authors in the XML was not sorted properly Several internal changes (adjusted logging table; click time is not updated any more for second clicks etc;an automatic tool to add stereotype recommendations)

By Joeran Beel, ago
Machine Learning

Some numbers about Mr. DLib’s Recommendations-as-a-Service (RaaS)

Six months ago, we launched Mr. DLib’s recommendations-as-a-service for Academia. Time, to look back and provide some numbers: Since September 2016, Mr. DLib´s recommender system has delivered 60,836,800 recommendations to our partner Sowiport, and Sowiport’s users have clicked 91,545 of the recommendations. This equals on overall click-through rate (CTR) of 0.15%. The figure shows the number of delivered recommendations and CTR by month (2016-09-08 to 2017-02-11).  CTR is rather low and there is a notable variance among the months (e.g. 0.21% in September and 0.10% in December). The variance may be caused by different algorithms we are experimenting with. In addition, recommendations are also delivered when web spiders such as Google Bot are crawling our partner website Sowiport.de. In contrast, clicks are Read more…

By Joeran Beel, ago
Machine Learning

Two of our papers about citation and term-weighting schemes got accepted at iConference 2017

Two of our papers about weighting citations and terms in the context of user modeling and recommender systems got accepted at the iConference 2017. Here are the abstracts, and links to the pre-print versions: Evaluating the CC-IDF citation-weighting scheme: How effectively can ‘Inverse Document Frequency’ (IDF) be applied to references? In the domain of academic search engines and research-paper recommender systems, CC-IDF is a common citation-weighting scheme that is used to calculate semantic relatedness between documents. CC-IDF adopts the principles of the popular term-weighting scheme TF-IDF and assumes that if a rare academic citation is shared by two documents then this occurrence should receive a higher weight than if the citation is shared among a large number of documents. Although CC-IDF Read more…

By Joeran Beel, ago