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…

Research-Paper Recommender Systems: A Literature Survey

“Research-Paper Recommender Systems: A Literature Survey” now available open access

“Research-Paper Recommender Systems: A Literature Survey”, our survey on recommender systems for research articles and citations is now available open access on Springer via ReadCube https://rdcu.be/5qT7. This survey is our most cited paper (241 citations according to Google Scholar), and we are glad that it is now available for free for 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…

Click-through rate (CTR) and # of delivered recommendation in JabRef for Mr. DLib’s (MDL) and CORE’s recommendation engine and in total

Mr. DLib’s Living Lab for Scholarly Recommendations (preprint)

We published a manuscript on arXiv about the first living lab for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for scholarly recommendations, i.e., research papers, citations, conferences, research grants etc. Recommendations are delivered through the living lab´s API in platforms such Read more…

'recommender systems ireland' Google results

Our website ranks #1 for ‘recommender systems ireland’ and ‘recommender systems dublin’ searches on Google

We started working at Trinity College Dublin 1.5 years ago and launched our new website only 2 months ago. Yet, Google ranks our website #1 for the search queries ‘recommender systems ireland‘ and ‘recommender systems dublin‘ and, not surprisingly, for the variations ‘ireland recommender systems‘ and ‘dublin recommender systems‘. Of course, this is not to mean that Read more…