Partnerships

Visiting Professorship: We intensify our collaboration with the NII in Tokyo

Today I was appointed as Visiting Professor at the National Institute of Informatics (NII), effective 1. April 2018 for the forthcoming four years. I am very grateful for the generous support of the NII, and I am looking forward to visiting the NII approximately once or twice a year for a few weeks to collaborate on research relating to recommender systems, machine learning, natural language processing and our other research areas. I have been working closely together with the NII in Tokyo for quite a while, in particular with the Digital Content and Media Division and Prof Dr Akiko Aizawa. As such, I am glad to continue and intensify the collaboration for at least four years.

By Joeran Beel, ago
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
Internal

Our new website is live!

Today, we launched our new website https://www.scss.tcd.ie/joeran.beel/. It provides lots of information about our research, publications, projects, and teaching relating to recommender systems, machine learning and more. The new website also combines the blog posts of our project websites Mr. DLib and Docear.

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
Mr. DLib

Mr. DLib Recommendations-as-a-Service v1.3: “Word Embeddings” and Many Minor Improvements and Bug Fixes

We released version 1.3 of Mr. DLib´s Recommender-System as-a-Service. The new major feature is “word embeddings” based recommendations. We are excited to see how the new recommendations will perform with our partners. In addition, we fixed many small bugs, and added some minor improvements.  A complete overview can be found in JIRA.

By Joeran Beel, ago
Conferences

Report from the 11th ACM Conference on Recommender Systems

We just returned from the 11th ACM Conference on Recommender Systems in Como, Italy. It was an amazing conference, with lots of interesting presentation relating to recommender systems. One of the hot topics at the Recommender Systems Conference was Deep Learning, though, frankly, deep learning did not always seem to deliver promising results for recommender systems. Here are a few photos.

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
Recommendations as-a-Service (RaaS)

Mr. DLib v1.1 released: JavaScript Client, 15 million CORE documents, new URL for recommendations-as-a-service via title search

We are proud to announce version 1.1 of Mr. DLib’s Recommender-System as-a-Service. The major new features are: A JavaScript Client to request recommendations from Mr. DLib. The JavaScript offers many advantages compared to a server-side processing of our recommendations. Among others, the main page will load faster while recommendations are requested in the background and a loading animation is shown. Using the JavaScript also means that the logging will be more reliable because web spiders are not logged any more. Our partner Sowiport uses the JavaScript already. We indexed 15 million documents from CORE and recommend them through our API. Another 5 million will follow soon. So far, recommendations could only be requested by specifying a particular document ID such as https://api-beta.mr-dlib.org/v1/documents/<ID>/related_documents/. Now, recommendations can Read more…

By Joeran Beel, ago