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…