Recommender-Systems.com: The Central Platform for the Recommender Systems Community? A Beta-Preview and Survey.

For beginners in recommender systems, it is difficult to get an overview of the state-of-the-art, the most relevant venues, and helpful tools and datasets. Similarly, experts in recommender-systems may find it difficult to keep up to date with the latest developments as relevant publications and news are often scattered across Read more…

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…

Darwin & Goliath, Recommendations As a Service in our Blog

Darwin & Goliath: A White-Label Recommender-System As-a-Service with Automated Algorithm-Selection

This is the pre-print of our upcoming publication at the 13th ACM Conference on Recommender Systems (RecSys’19). Joeran Beel, Alan Griffin, and Conor O’Shea. 2019. Darwin & Goliath: A White-Label Recommender-System As-a-Service with Automated Algorithm-Selection. In Proceedings of the 13th ACM Conference on Recommender Systems (RecSys’19). ACM, New York, NY, Read more…

4 of our Submissions Got Accepted at the ACM Recommender Systems Conference (RecSys’19)

Four of our poster and demo submissions got accepted for presentation at the 13th ACM Recommender Systems Conference (RecSys 2019) in Copenhagen in September. The accepted submissions are as follows (pre-prints will follow soon): Darwin & Goliath: A White-Label Recommender-System As-a-Service with Automated Algorithm-Selection Joeran Beel, Alan Griffin, Conor O’Shea Read more…

New Publication: Choice Overload and Recommendation Effectiveness in Related-Article Recommendations

The International Journal on Digital Libraries (IJDL) published our manuscript “Choice Overload and Recommendation Effectiveness in Related-Article Recommendations: Analyzing the Sowiport Digital Library”. The paper is freely available as open access via Springer. The paper is an extended version of a previous paper published at the 5th International Workshop on Read more…

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…

ELITE-S: 16 Postdoctoral Fellowships in ICT Standardisation — Recommender-Systems, (Automated) Machine Learning, APIs, …

Update: Deadline extended to July 15th, 2019. ELITE-S provides up to 16 postdoctoral fellowships to work for 2 years in the ADAPT Research Centre at Trinity College Dublin, University College Dublin, or other Irish universities as well as with an industry partner. ELITE-S is a EU Marie Skłodowska-Curie COFUND Action. Read more…

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…

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…

ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing (Pre-Print)

We are delighted to announce that our poster “ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing” has been accepted at the 12th ACM Recommender Systems Conference (RecSys) for presentation in Vancouver, Canada. The pre-print is available on arXiv, and here in our blog: Abstract Bibliographic reference parsers extract metadata (e.g. author names, Read more…