As part of the DAAD RISE Worldwide program, we were awarded two funded internship positions for two undergraduate students both being from Germany. The two interns will be conducting a research project as part of Mr. DLib in the fields of Read more…
This is another post in our series What Machine-Learning Students Think/Like/Know/Are …, which we began during teaching our Machine-Learning module at Trinity College Dublin, Ireland. Today, we present the machine-learning students’ experience and preferences of machine-learning libraries such as scikit-learn, Tensorflow, Read more…
The ACM Conference on Recommender Systems (RecSys) is the premier conference to present new research results, systems, and techniques including machine learning and natural language processing relating to recommender systems. This year, the 12th ACM Conference on Recommender Systems takes place Read more…
From time to time, I get asked by students “how can I find a good PhD supervisor?” And sometimes I sit on PhD evaluation panels and I am surprised how little thought the PhD students put into choosing their supervisor. Read more…
Students apparently had a strong focus on the application of machine learning. Students could each distribute 100 points to the different categories, and most students (24% of the points) wanted to learn about machine learning frameworks like scikit-learn, or Tensorflow. Read more…
The majority of the machine-learning students believed their presentation skills to be average (41%), good (25%) or even excellent (18%). Only a small proportion rated their skills as poor (9%) or very low (7%).
We published a proposal on ResearchGate (DOI: 10.13140/RG.2.2.18759.39842) for a novel approach to the calculate relatedness of research articles and other document types. The relatedness of research articles, patents, legal documents, web pages, and other documents is often calculated with citation Read more…
According to the students´ self-assessment, they had – on average – average knowledge of JAVA (3.2 out of 5 points). However, there was quite a bit of variance. Many students had very little knowledge of JAVA, while others had excellent knowledge. Read more…
The top careers for the machine-learning students were machine-learning engineer (16%), software engineer (16%), business start-up founder (14%), and data scientist (13%). However, also careers in project management (10%), research (9%) and consultancy (8%) were interesting to some students. “Business Read more…
On average, the machine-learning students had average knowledge in databases and general writing (around 3 out of 5 points), below-average knowledge in writing research papers (1.9 out of 5 points), and little knowledge in machine learning (1.5 out of 5). Read more…
10% of the students had very low motivation to participate in the module, while the majority of the students had high (28%) or even very high (48%) motivation to learn about machine learning.
Update 2018-07-31: We updated the Dropbox Link Google Scholar recently changed its layout, and as a consequence, Docear couldn’t fetch metadata anymore from Google Scholar for PDF files. Fortunately, one of our users (“Silberzwiebel”) adjusted Docear’s Google Scholar Parser, and now Read more…
We released version 1.3 of Mr. DLib´s Recommender-System as-a-Service. The new major feature is “word embeddings” based recommendations. We are excited to see how the new recommendations will perform with our partners. In addition, we fixed many small bugs, and Read more…
We just returned from the 11th ACM Conference on Recommender Systems in Como, Italy. It was an amazing conference, with lots of interesting presentation relating to recommender systems. One of the hot topics at the Recommender Systems Conference was Deep Read more…
We published a preprint on arXiv, in which we question the current practice of calculating evaluation metrics for recommender systems as single numbers (e.g. precision p=.28 or mean absolute error MAE = 1.21). We argue that single numbers express only Read more…
In the coming term, Prof. Douglas Leith and I will be teaching an introduction to machine learning here at Trinity College Dublin, Ireland. We expect around 200 students, primarily from the new Master of Science in Intelligent System and Data Read more…
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 Read more…
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 Read more…
There are two major news coming along with the new version of Mr. DLib’s Recommendation API. JabRef finally uses Mr. DLib for it’s recommender system We have announced this already a while ago, but now, finally, Mr. DLib’s recommendations are Read more…
Recently, we spoke with TechCentral.ie, Ireland’s best well-known and respected technology magazine, about recommender systems, personalisation, the challenges of the GDPR and our work in the field of recommender-systems as-a-service and machine learning. The full interview is available here http://www.techcentral.ie/focus-research-joeran-beel-adapt/
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 Read more…
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. Read more…
On 28th February, we released version 1.1.1 of Mr. DLib’s recommender system with some minor improvements and bug fixes: Improved 404 error handling for unknown document IDs Fix: The order of authors in the XML was not sorted properly Several Read more…
