Recommender Systems

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.

Tobias Vente presents the APS Explorer at ACM Recsys’25: Navigating Algorithm Performance Spaces for Informed Dataset Selection

We are excited to share that our PhD student, Tobias Vente, presented our research paper, “APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset Selection”, at the ACM RecSys 2025 conference held at the O2 Universum Convention Center in Prague, Czechia The Problem: Why RecSys Needs Better Dataset Selection An analysis of all full papers accepted at Read more…

By Intelligent Systems Group, ago

Our work on ‘Green Recommender Systems’ was just accepted for publication at ACM TORS

We are pleased to announce that our paper, “Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization,” has been accepted for publication in ACM Transactions on Recommender Systems (TORS). It is already available in the ACM Digital Library. About the Paper As recommender systems become increasingly powerful, Read more…

By Lukas Wegmeth, ago

ISG attends ACM RecSys 2025 in Prague

Last week, the ISG team—Prof. Joeran Beel, Moritz Baumgart, Lukas Wegmeth, Tobias Vente, and Philipp Meister—attended the ACM RecSys 2025 conference held at the O2 Universum Convention Center in Prague, Czechia. As the premier venue for recommender systems research, ACM RecSys gathers leading academic and industry groups to present state-of-the-art techniques, evaluation methods, Read more…

By Philipp.Meister, ago

Tobias Vente Presents Our Work on AutoML’s Potential for Recommender Systems at ACM UMAP

We are excited to share that our PhD student, Tobias Vente, presented our research paper, “The Potential of AutoML for Recommender Systems,” at the ACM UMAP 2025 conference held at the Verizon Executive Education Center at Cornell Tech on Roosevelt Island, New York City. Tobias presented our work in the Hyper Workshop (Hybrid AI for Human-Centric Personalization), which Read more…

By t_vente, ago

Lukas Wegmeth & Tobias Vente gave an invited talk on Green Recommender Systems at SFI MediaFutures in Bergen, Norway

Lukas Wegmeth & Tobias Vente, both fourth-year Ph.D. students at the Intelligent Systems Group, were invited to SFI MediaFutures in Bergen, Norway, to give a talk on the sustainability of Recommender Systems on April 4th, 2025. Prof. Sole Pera also gave an invited talk on responsible Recommender Systems on the Read more…

By Intelligent Systems Group, ago

From Clicks to Carbon: The Ecological Costs of Recommender Systems (Pre-Print)

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…

By Intelligent Systems Group, ago

Our Publication at the ECIR 2024: Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems

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…

By Intelligent Systems Group, ago

Prof. Beel at ‘Schloss Dagstuhl’ seminar 23031 ‘Frontiers of Information Access Experimentation for Research and Education’

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…

By Intelligent Systems Group, ago

Our Publication at the PERSPECTIVES 2022 Workshop @ RecSys 2022 – CaMeLS: Cooperative Meta-Learning Service for Recommender Systems

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…

By Lukas Wegmeth, ago

2 x Postdoctoral Researcher or Ph.D. Position in AutoML and/or Recommender Systems

— 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…

By Joeran Beel, ago

A First Analysis of Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems

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…

By andrew_collins, ago
Keyphrase counts and their effect on clickthrough rates (CTR)

Document Embeddings vs. Keyphrases vs. Terms: An Online Evaluation in Digital Library Recommender Systems

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…

By andrew_collins, ago
Algorithm selection for recommender systems using meta-learning

A Novel Approach to Recommendation Algorithm Selection using Meta-Learning

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…

By andrew_collins, ago

The Architecture of Mr. DLib’s Scientific Recommender-System API

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…

By Joeran Beel, ago
26th Irish Conference on Artificial Intelligence and Cognitive Science, hosted by Trinity College Dublin

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 Read more…

By Joeran Beel, ago
Homepage of the Working Group of Prof Dr Joeran Beel (Machine Learning and Recommender Systems

Our new website is live!

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.  

By Joeran Beel, ago