mr dlib – ISG Siegen

Keyphrase counts and their effect on clickthrough rates (CTR)

Mr. DLib

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, 5 years ago

Recommender Systems

V2 of Mr. DLib’s Recommender System As-a-Service is now available

The first beta version of Mr. DLib V2 has been released and is performing very well so far. With our partner JabRef, we have delivered over 70 thousand sets of recommendations (approximately 1,000 per day), or a total of over 450,000 individual recommendations. We have a click through rate (CTR) Read more…

By Joeran Beel, 5 years ago

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

Recommender Systems

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…

By Joeran Beel, 6 years ago

Recommender Systems

RARD II: The 2nd Related-Article Recommendation Dataset (preprint)

We released a new version of RARD, i.e. RARD II and describe the new release in a preprint published on arXiv. The dataset is available at http://data.mr-dlib.org and the new manuscript is available on arXiv and here in our Blog. The main contribution of this paper is to introduce and describe Read more…

By Joeran Beel, 6 years ago

Prof Henri Angelino (NII) and Prof Joeran Beel (TCD Dublin, Ireland) MoU Visit (Joint Recommender Systems Project)

Partnerships

MoU between the National Institute of Informatics (NII) and Trinity College Dublin (TCD) / SCSS / ADAPT

After being appointed as a visiting professor at the National Institute of Informatics (NII) a few weeks ago, there is more good news. The NII has signed a memorandum of understanding (MoU) with the School of Computer Science and Statistics (SCSS) of Trinity College Dublin (TCD) and the ADAPT Research Centre. The MoU originates Read more…

By Joeran Beel, 6 years ago

Mr. DLib

Mr. DLib v1.2.1: Improved keyphrase recommendations and Apache Lucene query handling

The new version of our recommender system completes 104 issues and significantly improves the recommendations. The most notable improvements are: We improved the keyphrase extraction process in the recommender system, i.e. keyphrases are not stored differently in Lucene. We expect better recommendation effectiveness and are currently running an A/B test. More Read more…

By Joeran Beel, 7 years ago

RARD I: The Related-Article Recommender-System Dataset

Mr. DLib

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…

By Joeran Beel, 7 years ago

Mr. DLib

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…

By Joeran Beel, 7 years ago

Recommendations as-a-Service (RaaS)

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…

By Joeran Beel, 7 years ago

Sakura in Tokyo (Internships in Recommender Systems and Machine Learning)

Help Wanted

Students & PostDocs: We have open positions in Tokyo, Copenhagen, and Konstanz (2-24 months)

Update 2016-01-12: The salary in Tokyo would be around 1.600 US$ per month, not 1.400. 2015 has been a rather quiet year for Docear, but 2016 will be different. We have lots of ideas for new projects, and even better – we have funding to pay at least 1 Master or Read more…

By Joeran Beel, 8 years ago