We welcome Teresa Scheidt, who joins our group for a research internship over the summer. Teresa is a Master student at Lund University, where she is currently in the first year of the Master program ‘Machine Learning, Systems and Control’. She completed her bachelor’s degree in ‘Medizintechnik’ (Medical Engineering) at Read more…
Abstract We introduce Recommender-Systems.com (RS_c) as a central platform for the recommender-systems community. RS_c provides regular news on important events in the community as well as curated lists of recommender-system resources including datasets, algorithms, jobs, software, and learning materials. Based on a survey with 28 participants – mostly authors at Read more…
For beginners in recommender systems, it is difficult to get an overview of the state-of-the-art, the most relevant venues, and helpful tools and datasets. Similarly, experts in recommender-systems may find it difficult to keep up to date with the latest developments as relevant publications and news are often scattered across Read more…
Two student groups in my E-Business module did a great job in developing an add-on for Gmail to distinguish genuine emails from mass emails (mass email not as in “spam” but as in an email sent e.g. by a student to dozens or hundreds of professors asking for open Ph.D. Read more…
Rohan Anand is a Masters’s student at the School of Computer Science and Statistics at Trinity College Dublin in Ireland and joins our group for the next eight months to conduct his Master Thesis. During his undergraduate program, Rohan published a paper titled BarGain: Crowd-Sourced Machine Learning Integrated E-Commerce. After Read more…
We welcome Haley, who will be joining us for the 2019/2020 Academic Year. Haley is an Undergraduate Computer Science & Business student here at Trinity College Dublin. She has just completed an international exchange in The University of Sydney Australia and is now enrolled in the final year of her Read more…
We were accepted for publication at ComplexRec 2019, the third workshop on Recommendation in Complex Scenarios at the 13th ACM Recommender Systems Conference (RecSys 2019) in Copenhagan, Denmark. Abstract. effectiveness of recommender system algorithms varies in different real-world scenarios. It is difficult to choose a best algorithm for a scenario Read more…
Abstract. Many recommender-system datasets are pruned, i.e. some data is removed that wouldn’t be removed in a production recommender-system. For instance, the MovieLens dataset contains only data from users who rated 20 or more movies.1 Similarly, some researchers prune data themselves and conduct their experiments only on subsets of the Read more…
Update 2019-08-12: Some numbers presented in this post were incorrect, and we updated them. For the latest full analysis, please refer to extended pre-print of Data Pruning in Recommender Systems Research: Best Practice or Malpractice? Datasets are a key ingredient to successful research in the field of recommender systems and Read more…
This is the pre-print of our upcoming publication at the 13th ACM Conference on Recommender Systems (RecSys’19). Joeran Beel, Alan Griffin, and Conor O’Shea. 2019. Darwin & Goliath: A White-Label Recommender-System As-a-Service with Automated Algorithm-Selection. In Proceedings of the 13th ACM Conference on Recommender Systems (RecSys’19). ACM, New York, NY, Read more…
Four of our poster and demo submissions got accepted for presentation at the 13th ACM Recommender Systems Conference (RecSys 2019) in Copenhagen in September. The accepted submissions are as follows (pre-prints will follow soon): Darwin & Goliath: A White-Label Recommender-System As-a-Service with Automated Algorithm-Selection Joeran Beel, Alan Griffin, Conor O’Shea Read more…
We are delighted to announce a seminar by Prof Theja Tulabandhula from the University of Illinois Chicago. The seminar will start at 10:00 am, Tuesday, 25 of June 2019 in the Small Conference Room in the O’Reilly Institute at Trinity College Dublin. Theja is visiting our lab and presents his Read more…
Trinity College Dublin has a new website and we are honored that our research about recommender-systems is featured as one of six research projects on TCD’s research page in the #researchMATTERS section.
A few months ago, we — Alan Griffin, Conor O’Shea, and I — started the private beta of our recommender-system as-a-service Darwin & Goliath. Now, we made it to the final round (10 start-ups) of The Ireland Funds Business Plan Competition, organized by NDRC. We are looking forward to a Read more…
I have been invited to give a talk at the LibTech Workshop at the 48th Annual LIBER Conference. The Library of Trinity College Dublin (TCD) has been cultivating an open innovation research model in partnership with a number of Trinity research centres. The ADAPT Centre and the Library are pleased Read more…
The International Journal on Digital Libraries (IJDL) published our manuscript “Choice Overload and Recommendation Effectiveness in Related-Article Recommendations: Analyzing the Sowiport Digital Library”. The paper is freely available as open access via Springer. The paper is an extended version of a previous paper published at the 5th International Workshop on Read more…
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…
Aicha is a PhD student of Computer Science at the University of Laghouat in Algeria. She arrives today and will work with us for 1.5 years. Aicha’s research interests include machine learning, natural language processing, and recommender systems. Aicha’s PhD topic is analyzing user engagement behaviour in online social networks Read more…
Update: Deadline extended to July 15th, 2019. ELITE-S provides up to 16 postdoctoral fellowships to work for 2 years in the ADAPT Research Centre at Trinity College Dublin, University College Dublin, or other Irish universities as well as with an industry partner. ELITE-S is a EU Marie Skłodowska-Curie COFUND Action. Read more…
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…
Our lab is glad to welcome Hebatallah Mohamed as a visiting PhD student for three months. Hebatallah is a PhD student at the Department of Computer Science at the University of Roma Tre in Italy. In Rome, she is doing her PhD under the supervision of Prof. Alessandro Micarelli in Read more…
Our paper “A Novel Approach to Recommendation Algorithm Selection using Meta-Learning” was accepted for publication at the 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS): Introduction The ‘algorithm selection problem’ describes the challenge of finding the most effective algorithm for a given recommendation scenario. Some typical recommendation scenarios are Read more…
Our manuscript “ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers” was accepted for publication at the 26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS). It is an extended version of our recently presented poster “ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing” at the ACM RecSys conference. The Read more…
Our manuscript “The Architecture of Mr. DLib’s Scientific Recommender-System API” got accepted at the “26th Irish Conference on Artificial Intelligence and Cognitive Science” (AICS), and here is the pre-print version (HTML below; PDF on arxiv). The bibliographic BibTeX data is: @InProceedings{Beel2018MDLArch, author = {Beel, Joeran and Collins, Andrew and Aizawa, Read more…
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…