Recommender Systems is one of our core competencies. We particularly focus on the development of real-world recommender systems, user modelling, the evaluation of recommender systems, and several aspects more (e.g. labelling of recommendations, recommendation persistence, position bias, choice overload, and recommender system datasets). For more details on our work on recommender systems read the blog posts below or have a look at the menu above. You find our main website at http://recommender-systems.ie and the current page at http://ireland.recommender-systems.ie.
This article (PDF Version) was part of the recent Dagstuhl Seminar “Evaluation Perspectives of Recommender Systems: Driving Research and Education”. The full report should be published soon. Cite as follows: I recall vividly when more than a decade ago – I was a PhD student – Konstan & Adomavicius warned Read more…
Full pre-print as PDF: https://arxiv.org/abs/2408.08203 Abstract As global warming soars, the need to assess the environmental impact of research is becoming increasingly urgent. Despite this, few recommender systems research papers address their environmental impact. In this study, we estimate the ecological impact of recommender systems research by reproducing typical experimental Read more…
Recently, we were delighted to learn that our paper “Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems” was accepted for the “Full Paper” track at the ECIR 2024. The paper, co-authored by members of our chair, the Intelligent Systems Group, will be presented by one of Read more…
As part of my research visit to the University of Gothenburg, I am glad to announce that I will be giving a seminar on “The ‘unreasonable’ effectiveness of Graphical User Interfaces for Recommender Systems”. The seminar will take place at the University of Gothenburg, on Friday, 8th of September, at Read more…
Update 2023-05: The report is now available on arXiv.org. Prof. Beel was invited to attend the Dagstuhl seminar about ‘Frontiers of Information Access Experimentation for Research and Education‘. The seminar took place from January 15 to January 20, 2023 in Schloss Dagstuhl, and was organized by Christine Bauer (Utrecht University, Read more…
With great delight, we had the opportunity to discuss our paper on our vision of a novel way to utilize the advantages of meta-learning in recommender systems at the 2nd Workshop: Perspectives on the Evaluation of Recommender Systems @ ACM Recommender Systems 2022. We had plenty of amazing discussions on Read more…
We welcome Tobias Vente as a new full-time Ph.D. student at the Intelligent Systems Group at the University of Siegen. Tobias completed his bachelor’s and master’s degree at the University of Siegen. He took part in the University of Siegen’s exchange program and spent one year of his master’s degree Read more…
Our group — the Intelligent Systems Group at the University of Siegen in Germany — seeks to fill 1 open position for either a postdoctoral researcher or a Ph.D. student. The starting date is flexible but we would prefer it as soon as possible. Your field of research would be flexible (there is no Read more…
— Applications Closed — Visit https://isg.beel.org/jobs/ for latest openings — Our group — the Intelligent Systems Group at the University of Siegen in Germany — recently acquired a €1.25m research grant. Therefore, we have 2 open positions for either postdoctoral researchers or PhD students. Your field of research generally would be very flexible (there Read more…
We were accepted for publication at ComplexRec 2019, the third workshop on Recommendation in Complex Scenarios at the 13th ACM Recommender Systems Conference (RecSys 2019) in Copenhagan, Denmark. Abstract. effectiveness of recommender system algorithms varies in different real-world scenarios. It is difficult to choose a best algorithm for a scenario Read more…
Our paper “Document Embeddings vs. Keyphrases vs. Terms: An Online Evaluation in Digital Library Recommender Systems” was accepted for publication at the ACM/IEEE Joint Conference on Digital Libraries. 1 Introduction Many recommendation algorithms are available to operators of recommender systems in digital libraries. The effectiveness of algorithms in real-world systems is Read more…
Our paper “A Novel Approach to Recommendation Algorithm Selection using Meta-Learning” was accepted for publication at the 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS): Introduction The ‘algorithm selection problem’ describes the challenge of finding the most effective algorithm for a given recommendation scenario. Some typical recommendation scenarios are Read more…
A colleague of mine has initiated a new research project to analyse smartphone users’ usage behaviour and personality. He and his team have released an Android app named TYDR: Track Your Daily Routine. With the data from TYDR, they want to research if they can estimate the personality of a smartphone user Read more…
Our manuscript “The Architecture of Mr. DLib’s Scientific Recommender-System API” got accepted at the “26th Irish Conference on Artificial Intelligence and Cognitive Science” (AICS), and here is the pre-print version (HTML below; PDF on arxiv). The bibliographic BibTeX data is: @InProceedings{Beel2018MDLArch, author = {Beel, Joeran and Collins, Andrew and Aizawa, Read more…
Today, the 12th ACM Conference on Recommender Systems began in Vancouver, Canada. We attend and will present our work on meta-learning reference parsing tools in which we treat reference extraction from scientific articles as a recommendation problem. If you also attend the conference, visit us during the poster session on Thursday. So Read more…
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 Read more…
Today, we launched our new website https://ISG.beel.org/. 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.
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 Read more…
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 Read more…
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 Read more…
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 Read more…
From time to time, I get asked by students “how can I find a good PhD supervisor?” And sometimes I sit on PhD evaluation 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 Read more…
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 Read more…
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 Read more…
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 Read more…