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
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
Recommender Systems

Seminar by Prof Dietmar Jannach: “Recommender Systems – Beyond Matrix Completion”

We are delighted to announce a seminar by Prof Dr Dietmar Jannach on Recommender Systems at Trinity College Dublin. Dietmar Jannach is a well-known researcher in the field of recommender systems and author of the book “Recommender Systems: An Introduction“. The seminar is open to all staff, students, and visitors in Dublin who are interested in recommender systems. Title: Recommender Systems – Beyond Matrix Completion Abstract: Automated recommendations have become a common part of our daily online user experience. Significant advances were made in recent years in terms of algorithmic approaches to compute recommendations for users. The main task in such an algorithm-focused setting is to predict through machine learning approaches how relevant a certain item will be for an Read more…

By Joeran Beel, ago
Jobs & Internships

We welcome two DAAD interns in recommender systems and machine learning (Dublin & Tokyo)

As part of the DAAD RISE Worldwide program, we were awarded two funded internship positions for two undergraduate students both being from Germany. The two interns will be conducting a research project as part of Mr. DLib in the fields of recommender systems, machine learning and natural language processing. Gordian (University of Munich / LMU) and Martin (Universty of Göttingen) will spend around three months with us over the summer — Gordian at the National Institute of Informatics in Tokyo, and Martin at the ADAPT Centre and School of Computer Science at the Trinity College Dublin.

By Joeran Beel, ago
Conferences

Call for Papers: ACM Recommender Systems Conference 2018 in Vancouver

The ACM Conference on Recommender Systems (RecSys) is the premier conference to present new research results, systems, and techniques including machine learning and natural language processing relating to recommender systems.  This year, the 12th ACM Conference on Recommender Systems takes place in Vancouver, and the call for papers has just been published. We are proud to serve on the program committee and hope to receive many interesting submissions this year.

By Joeran Beel, ago
Academia

What makes a great PhD supervisor (for recommender-systems and machine-learning research)?

From time to time, I get asked by students “how can I find a good PhD supervisor? What are the key characteristics?” And sometimes, I sit on PhD panels, and I am surprised how little thought the PhD students put into choosing their supervisor. Therefore, I decided to write this blog post for potential PhD candidates about choosing a good PhD supervisor. I hope it provides guidance to those who plan to do a PhD. In my description, I focus on potential PhD students in information retrieval, machine learning and recommender systems because these are the fields I am working in. However, most of the information probably also applies to PhD studies in other fields. Before I explain the important 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
Mr. DLib

Mr. DLib v1.2.1: Improved keyphrase recommendations and Apache Lucene query handling

The new version of our recommender system completes 104 issues and significantly improves the recommendations. The most notable improvements are: We improved the keyphrase extraction process in the recommender system, i.e. keyphrases are not stored differently in Lucene. We expect better recommendation effectiveness and are currently running an A/B test. More robust path encoding for search queries (special characters in a URL caused errors) Lucene’s eDismax function is A/B tested (together with Lucene’s standard query parser) Improved queries for CORE recommender system (their system needs queries to be of a certain length; Mr. DLib now just multiplies the queries until they are at least 50 characters) Abstracts and keywords in the XML response of Mr. DLib are enclosed in <![CDATA[ HTML Snippet is improved Read more…

By Joeran Beel, ago