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Conferences

Report from the 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science

The 26th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS) is over, and it was a full success. This year, AICS celebrated its 30th anniversary and was hosted by Trinity College Dublin’s School of Computer Science and Statistics, the School of Psychology and the Institute of Neuroscience. I had the honour to co-organize the conference together with Rob Brennan (General Co-Chair), Ruth Byrne (Cognitive Science Chair) and Jeremy Debattista (Publication Chair). AICS took place on the Trinity campus at the Trinity Long Room Hub, Trinity’s interdisciplinary arts and humanities research institute. The following text is based on [3]. While once a niche area, the fields of Cognitive Science and Artificial Intelligence, which encompass Data Analytics, Information Retrieval, and Machine Learning, are now Read more…

Machine Learning

A Novel Approach to Recommendation Algorithm Selection using Meta-Learning

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 news websites [3], digital libraries [4, 5], movie-streaming platforms [13]. The performance of recommender system algorithms vary in these different scenarios [3, 6, 10, 11, 15] as illustrated in Fig. 1. Performance variation occurs for many reasons, for example, the effectiveness of collaborative filtering algorithms changes depending on the number of ratings available from users [10]. Algorithms also perform differently depending on the demographic characteristics of users [6][11], depending on Read more…

Jobs / Career

“China Scholarship Council – Trinity College Dublin Joint Scholarship” now open for applications

As in previous years, Trinity College Dublin has up to 10 scholarships to award for PhD positions as part of the China Scholarship Council – Trinity College Dublin Joint Scholarship Programme. Each scholarship will include a full fee-waiver from Trinity, and a stipend for living costs (currently €1,200 per month), airfare and health insurance, provided by the CSC, this does not include bench fees. Applicants must be Chinese nationals but can be registered on Masters programmes either within China or at an overseas institution (including Trinity). To be eligible for the scheme, students should have received an unconditional offer letter for their PhD position at Trinity College Dublin. Conditional offers letters may be accepted but only subject to meeting academic Read more…

Seminars

Serving Customer Insights in Zalando (Upcoming Presentation in our Machine Learning Lecture)

After announcing a guest presentation from Zalando in our e-Business lecture, we are delighted to announce another talk by Zalando, this time in our machine learning lecture. Antoaneta Marinova from Zalando, will give a presentation on 27th November at 15:00 o’clock. Antoaneta is a Data Engineer in the Customer Fashion Profile team. She works at Zalando for two years, mainly on attribute recommendations and customer segmentation. Previously, she was working on ad optimisation for Adcash and as a software developer in Axway. She has a masters degree in Artificial Intelligence and a bachelor in Computer Science from Sofia University. Antoaneta´s talk is titled “Serving Customer Insights in Zalando“. The abstract is as follows. The presentation will focus on Zalando’s Customer Fashion Read more…

Seminars

Re-Platforming a €5-Billion Company, with Zero Downtime. The Zalando Story! (Upcoming Presentation)

I am delighted to announce that Conor Gallagher from Zalando Ireland will be giving a presentation in my e-Business II lecture on 27th of November at 11:00 o’clock.  Conor is Senior Engineer and Team Lead on the Zalando re-platforming project. The presentation is titled “Re-Platforming a €5 billion company, with zero downtime. The Zalando story!” and the abstract is as follows: In 10 short years, Zalando.com grew from a small site selling flip-flops to the largest online Fashion retailer in Europe with 24.6 million active customers, growing by 20-25% year on year. This rapid expansion has necessitated a re-design and migration of the software systems powering Zalando.com to cope with the ever-increasing load on our site. This talk will provide a Read more…

Machine Learning

Accepted Workshop @ECIR2019: The 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR)

Our proposal for the “1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR)“, to be held at the  41st European Conference on Information Retrieval (ECIR), was accepted. AMIR will take place on the 14th of April 2019 in Cologne, Germany. The algorithm selection problem describes the challenge of identifying the best algorithm for a given problem space. In many domains, particularly artificial intelligence, the algorithm selection problem is well studied, and various approaches and tools exist to tackle it in practice. Especially through meta-learning impressive performance improvements have been achieved. The information retrieval (IR) community, however, has paid little attention to the algorithm selection problem, although the problem is highly relevant in information retrieval. AMIR will bring Read more…

Machine Learning

ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers

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 bibliographic BibTeX data is as follows. @InProceedings{TkaczykBeel2018, author = {Tkaczyk, Dominika and Gupta, Rohit and Cinti, Riccardo and Beel, Joeran}, title = {ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers}, booktitle = {26th Irish Conference on Artificial Intelligence and Cognitive Science (AICS)}, year = {2018}, volume = {5}, number = {1}, pages = {31–42}, } 1. Introduction Bibliographic reference parsing is a well-known task in scientific information extraction Read more…

Research

Participate in the “Track Your Daily Routine” Research Study About Your Smartphone Usage and Your Personality

A colleague of mine has initiated a new research project to analyse smartphone users’ usage behaviour and personality. He and his team have released an Android app named TYDR: Track Your Daily Routine. With the data from TYDR, they want to research if they can estimate the personality of a smartphone user by the data that can be collected automatically. This could eventually lead to not needing to fill out questionnaires anymore. There are potential benefits for mobile health apps and recommender systems – apps could know what type of person the user is and adapt to his/her needs. The main features include: Personality evaluation Visualization of your visited locations Photo statistics Music statistics Calls statistics Steps taken statistics App usage statistics Read more…

Recommendations as-a-Service (RaaS)

The Architecture of Mr. DLib’s Scientific Recommender-System API

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, Akiko}, title = {The Architecture of Mr. DLib’s Scientific Recommender-System API}, booktitle = {Proceedings of the 26th Irish Conference on Artificial Intelligence and Cognitive Science}, year = {2018}, volume = {26}, volumes = {1}, number = {26}, pages = {1–12}, abstract = {Recommender systems in academia are not widely available. This may be in part due to the difficulty and cost of developing and maintaining recommender systems. Many operators of Read more…

Machine Learning

Best-Paper Award for our Publication “Implementing Neural Turing Machines” at the 27th International Conference on Artificial Neural Networks

We received the best-paper award at the 27th International Conference on Artificial Neural Networks (ICANN 2018) for our paper Implementing Neural Turing Machines. Our student, and co-author of the paper, Mark Collier was at ICANN to present our work about implementing a Neural Turing Machine. Neural Turing Machines (NTMs) are an instance of Memory Augmented Neural Networks, a new class of recurrent neural networks which decouple computation from memory by introducing an external memory unit. Neural Turing Machines have demonstrated superior performance over Long Short-Term Memory Cells in several sequence learning tasks. A number of open source implementations of Neural Turing Machines exist but are unstable during training and/or fail to replicate the reported performance of NTMs. This paper presents the details Read more…

Recommender Systems

Report/Photos from the 12th ACM Conference on Recommender Systems in Vancouver

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 far, it has been a great conference with many exciting talks. A few of the many great presentations are the following Why I like it: Multi-task Learning for Recommendation and Explanation We describe a novel, multi-task recommendation model, which jointly learns to perform rating prediction and recommendation explanation by combining matrix factorization, for rating prediction, and adversarial sequence to sequence learning for explanation generation. The result is evaluated using real-world Read more…

Machine Learning

An Empirical Comparison of Syllabuses for Curriculum Learning (Pre-Print)

Update 12/11/2018: Our paper has been accepted at AICS 2018 and will be presented at the conference in December. We have published a pre-print (now available on Arxiv) which outlines our work comparing different syllabuses for curriculum learning. Neural networks are typically trained by repeatedly randomly selecting examples from a dataset and taking steps of stochastic gradient descent. Curriculum learning is an alternative approach to training neural networks, inspired by human learning in which training examples are presented according to a syllabus typically of increasing “difficulty”. Curriculum learning has shown some impressive empirical results, but little is known about the relative merits of different syllabuses. In this work, we provide an empirical comparison of a number of syllabuses found in Read more…

Machine Learning

Proposal for the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR)

Lars Kotthoff and I have applied to organize the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR) at the 41st European Conference on Information Retrieval (ECIR). Let’s cross fingers and hope it will get accepted. In the following, you find the proposal (also available on ResearchGate as PDF). @InProceedings{BeelKotthoff2018, author = {Beel, Joeran and Kotthoff, Lars}, title = {Proposal for the 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR)}, booktitle = {ResearchGate Repository}, year = {2018}, pages = {1–6}, doi = {10.13140/RG.2.2.14548.65922}, url = {https://www.researchgate.net/publication/328965675_Proposal_for_the_1st_Interdisciplinary_Workshop_on_Algorithm_Selection_and_Meta-Learning_in_Information_Retrieval_AMIR}, abstract = {The algorithm selection problem describes the challenge of identifying the best algorithm for a given problem space. In many domains, particularly artificial intelligence, the algorithm selection problem Read more…

Jobs & Internships

Open Call for “Government of Ireland Postgraduate Scholarship Programme 2019”

The Irish Research Council (IRC) has published a new call for its “Government of Ireland Postgraduate Scholarship” programme. We are eligible to supervise applicants during their application process and act as PhD supervisor for the 4-year period of the scholarship. Hence, if you are interested in doing a PhD at Trinity College Dublin in the field of machine learning or recommender systems, or any other of our research fields, please contact us. From their website The Government of Ireland Postgraduate Scholarship Programme is an established national initiative, funded by the Department of Education and Skills and managed by the Council. In 2017, we invested in a total of 1,179 postgraduate scholars, with over 5,000 individual scholarships for excellent research awarded Read more…

Machine Learning

3rd Call for EU Marie Curie EDGE Fellowships (e.g. in Machine-Learning or Recommender-Systems Research)

The 3rd call for EU Marie Curie EDGE fellowships has been published. We successfully supported the application of one EDGE fellow a year ago, and we would be happy to support talented candidates this year, too. So, if you are interested in an EDGE fellowship in the field of machine learning, recommender systems or another of our research areas, please contact us. Let us know some details about your yourself and your project idea. EDGE is Marie Skłodowska-Curie COFUND Action, led by Trinity College Dublin on behalf of a group of academic institutions from across Ireland. EDGE will offer 71 prestigious Fellowships for experienced researchers (post-doctoral or equivalent) relocating to Ireland. EDGE is also a training and development programme for scientific excellence, offering a Read more…

Machine Learning

Trinity College Dublin seeks to hire a Professor (Chair) in Intelligent Systems

The University of Dublin, Trinity College, invites applications for the position of Professor of Intelligent Systems. The successful candidate will provide strong academic leadership in research, teaching and supervision. The Professor will strengthen the strategic research areas of Artificial Intelligence and Intelligent Content in the School of Computer Science and Statistics, and provide additional leadership in the SFI Research Centre for Digital Content Technology – ADAPT (www.adaptcentre.ie), hosted in the School. It is essential that the successful candidate will be an internationally recognized scholar in at least one of the following research areas: artificial intelligence; digital media and content analytics; knowledge and data engineering. These areas include scientific areas such as: Machine Learning; Natural Language Processing; Semantic Modelling; Personalisation; Data Read more…

Machine Learning

ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing (Pre-Print)

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, title, year) from bibliographic reference strings. No reference parser consistently gives the best results in every scenario. For instance, one tool may be best in extracting titles, and another tool in extracting author names. In this paper, we address the problem of reference parsing from a recommender-systems perspective. We propose ParsRec, a meta-learning approach that recommends the potentially best parser(s) for a given reference string. We evaluate ParsRec on 105k Read more…

Machine Learning

A Stable Neural-Turing-Machine (NTM) Implementation (Source Code and Pre-Print)

Update 2018-10-20: Official publication available at SpringerLink Update: 2018-10-06: Our paper received the best-paper award at ICANN.   We have released an open source implementation of a Neural Turing Machine for TensorFlow, and published on arXiv the corresponding paper which we will present at ICANN 2018. Neural Turing Machines are notoriously difficult to implement and train. Our contribution is to explain why previous implementations have not successfully replicated the results in the original Neural Turing Machines paper. We then went on and produced an open source implementation which trains reliably and quickly. The work was done as part of my undergraduate thesis in the group of Prof Joeran Beel at the School of Computer Science and Statistics and the ADAPT Centre at Trinity Read more…

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 as reference management software and digital libraries. The living lab is built on top of the recommender system as-a-service Mr. DLib. Current partners are the reference management software JabRef and the CORE research team. We present the architecture of Mr. DLib’s living lab as well as usage statistics on the first ten months of operating it. During this time, 970,517 recommendations were delivered with a mean click-through rate of 0.22%. Read more…

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 a new recommender-systems dataset (RARD II). It is based on data from a recommender-system in the digital library and reference management software domain. As such, it complements datasets from other domains such as books, movies, and music. The RARD II dataset encompasses 89m recommendations, covering an item-space of 24m unique items. RARD II provides a range of rich recommendation data, beyond conventional ratings. For example, in addition to the usual rating Read more…

Partnerships

2-week visit at the NII Tokyo: Dagstuhl-Seminar Presentation, Recommender-System and Machine-Learning Research, …

From the 19th of June until the 2nd of July 2018, I am in Japan at the National Institute of Informatics (NII). It is my first visit as Visiting Professor at the NII, and I am working with Professor Akiko Aizawa on a recommender-system and machine-learning project. Besides that work, there are many interesting presentations and other visitors at the NII. Among others, Prof. Dr. Raimund Seidel is visiting. Prof. Seidel is the organizer of the prestigious Dagstuhl seminar. Dagstuhl enables leading researchers in computer science meet for a few days to discuss the latest advancements and challenges in their fields. The NII is organizing a similar seminar series, the Shonan seminars, and Prof. Seidel shared his experience with organizing the Dagstuhl in a Read more…

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 from our long-term collaboration with the NII and outlines a partnership between TCD/SCSS/ADAPT and the NII. The goal of the partnership is to: Engage in joint efforts in research Work together in the area of institute management Exchange administrative and managerial staff Collaborate with industry and agencies for public administration and public services Exchange of research and academic staff and students We begin the collaboration with two projects. First, PhD Read more…

Machine Learning

Aiur by Iris.ai: Solving the Problems of Science via the Blockchain and Artificial Intelligence

I am on the advisory board of Iris.ai, a business start-up in the field of open science, blockchain, and artificial intelligence. Iris.ai was founded in 2015 and since then has released several exciting products that have the potential to revolutionize how scientists work. With their latest project, Project Aiur, the team behind Iris.ai envisions a world where the right scientific knowledge is available at our fingertips; where all research is validated and reproducible; where interdisciplinary connections are the norm; where unbiased scientific information flows freely; where research already paid for with our tax money is freely accessible to all. To realize Project Aiur, Iris.ai sells “AIUR tokens”, a new currency that could be the central currency in a new world of Read more…