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’ on Google (in Ireland)

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…

RARD I: The Related-Article Recommender-System Dataset

RARD: The Related-Article Recommendation Dataset

We are proud to announce the release of ‘RARD’, the related-article recommendation dataset from the digital library Sowiport and the recommendation-as-a-service provider Mr. DLib. The dataset contains information about 57.4 million recommendations that were displayed to the users of Sowiport. Information includes details on which recommendation approaches were used (e.g. content-based Read more…

Our Recommender-Systems Domains (recommender-systems.ie, recommender-systems.de, recsys.ie)

New domain names for our website: recommender-systems.ie and recsys.ie

We successfully registered the domains recommender-systems.ie and recsys.ie (in addition to our already registered domain recommender-systems.de and domain relating to machine-learning). For now, all domains point to our main website https://ISG.beel.org/. In the long run, we may use these domains for more specific purposes relating to recommender systems. Actually, we hope to Read more…

Several new publications: Mr. DLib, Lessons Learned, Choice Overload, Bibliometrics (Mendeley Readership Statistics), Apache Lucene, CC-IDF, TF-IDuF

In the past few weeks, we published (or received acceptance notices for) a number of papers related to Mr. DLib, research-paper recommender systems, and recommendations-as-a-service. Many of them were written during our time at the NII or in collaboration with the NII. Here is the list of publications: Beel, Joeran, Bela Gipp, Read more…

Mr. DLib v1.1 released: JavaScript Client, 15 million CORE documents, new URL for recommendations-as-a-service via title search

We are proud to announce version 1.1 of Mr. DLib’s Recommender-System as-a-Service. The major new features are: A JavaScript Client to request recommendations from Mr. DLib. The JavaScript offers many advantages compared to a server-side processing of our recommendations. Among others, the main page will load faster while recommendations are requested in the Read more…

Paper accepted at ISI conference in Berlin: “Stereotype and Most-Popular Recommendations in the Digital Library Sowiport”

Our paper titled “Stereotype and Most-Popular Recommendations in the Digital Library Sowiport” is accepted for publication at the 15th International Symposium on Information Science (ISI) in Berlin. Abstract: Stereotype and most-popular recommendations are widely neglected in the research-paper recommender-system and digital-library community. In other domains such as movie recommendations and hotel Read more…

Enhanced re-ranking in our recommender system based on Mendeley’s readership statistics

Content-based filtering recommendations suffer from the problem that no human quality assessments are taken into account. This means a poorly written paper ppoor would be considered equally relevant for a given input paper pinput as high-quality paper pquality if pquality and ppoor contain the same words. We elevate for this problem by using Mendeley’s readership data Read more…