Today, the 12th ACM Conference on Recommender Systems began in Vancouver, Canada. We attend and will present our work on meta-learning reference parsing tools in which we treat reference extraction from scientific articles as a recommendation problem. If you also attend the conference, visit us during the poster session on Thursday. So Read more…
“Research-Paper Recommender Systems: A Literature Survey”, our survey on recommender systems for research articles and citations is now available open access on Springer via ReadCube https://rdcu.be/5qT7. This survey is our most cited paper (241 citations according to Google Scholar), and we are glad that it is now available for free for Read more…
We are delighted to announce that our poster “ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing” has been accepted at the 12th ACM Recommender Systems Conference (RecSys) for presentation in Vancouver, Canada. The pre-print is available on arXiv, and here in our blog: Abstract Bibliographic reference parsers extract metadata (e.g. author names, Read more…
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…
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…
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…
We recently had a paper accepted to iConference. We used click data from Mr DLib, our recommender-as-a-service, to see if users of recommender systems in digital libraries are affected by position bias. We found that users are affected by position bias on average. Some users do seem to examine recommendations Read more…
UPDATE: We will soon advertise another position for this start-up. Please come back in a few days. The School of Computer Science and Statistics of Trinity College Dublin and the ADAPT Centre received funding to hire 2 employees for 2 years* to spin-out a business start-up in the field of Read more…
We are delighted to announce a seminar by Prof Dr Dietmar Jannach on Recommender Systems at Trinity College Dublin. Dietmar Jannach is a well-known researcher in the field of recommender systems and author of the book “Recommender Systems: An Introduction“. The seminar is open to all staff, students, and visitors 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…
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…
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…