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 everything works as usual. However, we have not yet integrated 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 added some minor improvements. A complete overview can be found 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 Learning, though, frankly, deep learning did not always seem to deliver promising 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 average effectiveness over usually a rather long period (e.g. a 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 Science. Many students will already have a background in machine 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 recommendation effectiveness and are currently running an A/B test. More 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 includes details on which recommendation approaches were used (e.g. content-based 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 available in one of the most popular open-source reference managers, 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 specific purposes relating to recommender systems. Actually, we hope to 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. Here is the list of publications: Beel, Joeran, Bela Gipp, 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 internal changes (adjusted logging table; click time is not updated any Read more…
Stefan Feyer from the University of Konstanz had joined the Mr. DLib team for a six months internship. Yesterday, it was time to say goodbye to Stefan and we had a small farewell party at the National Institute of Informatics (NII) in Tokyo. We wish Stefan all the best for his Read more…
Six months ago, we launched Mr. DLib’s recommendations-as-a-service for Academia. Time, to look back and provide some numbers: Since September 2016, Mr. DLib´s recommender system has delivered 60,836,800 recommendations to our partner Sowiport, and Sowiport’s users have clicked 91,545 of the recommendations. This equals on overall click-through rate (CTR) of Read more…
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…
Currently, all of Docear’s online services are down, including the recommender system. This means, you cannot register, log-in to download backups, or receive recommendations. As we have no time right now for the development of Docear, we are afraid that we won’t be able to fix this problem anytime soon. Read more…
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…
This semester my colleague and I are teaching the Machine-Learning module CS7CS4 at Trinity College Dublin. We decided to publish the lecture slides on Slideshare. The slides contain 11 lectures about machine learning, covering aspects such as an introduction to machine learning, standard machine learning algorithms (regression, support vector machines, k-means, Read more…
Two of our papers about weighting citations and terms in the context of user modeling and recommender systems got accepted at the iConference 2017. Here are the abstracts, and links to the pre-print versions: Evaluating the CC-IDF citation-weighting scheme: How effectively can ‘Inverse Document Frequency’ (IDF) be applied to references? In Read more…
A demonstration paper about the integration of Mr. DLib in JabRef is accepted for publication at ECIR 2017. We will update this post soon with more information and a pre-print.
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…
We are glad to announce that JabRef – one of the most popular open-source reference manager – will soon be using Mr. DLib´s recommender system to provide article recommendations to its users. The team of Mr. DLib and JabRef had several Skype calls, and we finally agreed about a concept how to integrate Read more…
Our servers for Mr. DLib’s recommender-system as-a-service (RaaS) are now monitored by UptimeRobot, a free monitoring service. You can access all our RaaS server statuses at this URL https://stats.uptimerobot.com/WLL5PUjN6 and you will see a dashboard like this: A click on one of the server names will show you more details, e.g. https://stats.uptimerobot.com/WLL5PUjN6/778037437
