Barry Smyth

Barry Smyth
University College Dublin | UCD · School of Computer Science

BSc, Computer Science, PhD

About

580
Publications
89,160
Reads
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15,181
Citations
Introduction
I hold the DIGITAL Chair of Computer Science at University College Dublin and I am currently the CEO of the Insight Centre for Data Analytics.
Additional affiliations
January 1995 - December 2012
University College Dublin
Position
  • Digital Chair of Computer Science
January 1992 - December 1995
Trinity College Dublin
Position
  • PhD Student

Publications

Publications (580)
Article
Full-text available
Aim This study characterised the decoupling of internal-to-external workload in marathon running and investigated whether decoupling magnitude and onset could improve predictions of marathon performance. Methods The decoupling of internal-to-external workload was calculated in 82,303 marathon runners (13,125 female). Internal workload was determin...
Conference Paper
Full-text available
Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail. Traditionally , financial forecasting has heavily relied on quantitative indicators and metrics derived from structure...
Preprint
Full-text available
Identifying meaningful relationships between the price movements of financial assets is a challenging but important problem in a variety of financial applications. However with recent research, particularly those using machine learning and deep learning techniques, focused mostly on price forecasting, the literature investigating the modelling of a...
Preprint
Full-text available
Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail. Traditionally, financial forecasting has heavily relied on quantitative indicators and metrics derived from structured...
Chapter
Modern universities present students with a dizzying array of course and module options, making it difficult for students to make informed decisions about the modules they take and how their choices can help them achieve their educational goals. This is exacerbated when students are uncertain about their goals or when limited information about modu...
Chapter
When training for endurance activities, such as the marathon, the risk of injury is ever-present, especially for first-time or inexperienced athletes. And because injuries depend on various factors, there is an opportunity to provide athletes with greater levels of support and guidance when it comes to the risks associated with their training. Henc...
Chapter
Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and prices. Nevertheless it has proven to be an attractive target for machine learning research because of the potential for even modest levels of prediction accuracy to deli...
Chapter
Full-text available
Ancient oracle bone inscriptions (OBIs) are important Chinese cultural artefacts, which are difficult and time-consuming to decipher even by the most expert paleographers and, as a result, a large proportion of excavated OBIs remain unidentified. In practice, OBIs are deciphered by translating between different writing systems; Chinese writing syst...
Chapter
Climate change poses a major challenge to humanity, especially in its impact on agriculture, a challenge that a responsible AI should meet. In this paper, we examine a CBR system (PBI-CBR) designed to aid sustainable dairy farming by supporting grassland management, through accurate crop growth prediction. As climate changes, PBI-CBR’s historical c...
Article
Full-text available
For marathoners the taper refers to a period of reduced training load in the weeks before race-day. It helps runners to recover from the stresses of weeks of high-volume, high-intensity training to enhance race-day performance. The aim of this study was to analyse the taper strategies of recreational runners to determine whether particular forms of...
Article
Full-text available
Every year millions of people, from all walks of life, spend months training to run a traditional marathon. For some it is about becoming fit enough to complete the gruelling 26.2 mile (42.2 km) distance. For others, it is about improving their fitness, to achieve a new personal-best finish-time. In this paper, we argue that the complexities of tra...
Conference Paper
In recent years, there has been an explosion of AI research on counterfactual explanations as a solution to the problem of eXplainable AI (XAI). These explanations seem to offer technical, psychological and legal benefits over other explanation techniques. We survey 100 distinct counterfactual explanation methods reported in the literature. This su...
Preprint
Full-text available
Forecasting stock returns is a challenging problem due to the highly stochastic nature of the market and the vast array of factors and events that can influence trading volume and prices. Nevertheless it has proven to be an attractive target for machine learning research because of the potential for even modest levels of prediction accuracy to deli...
Conference Paper
Full-text available
While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their training and test data. Such issues come to be manifest in performance problems when faced...
Preprint
Full-text available
While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their training and test data. Such issues come to be manifest in performance problems when faced...
Preprint
Full-text available
The explosion in the sheer magnitude and complexity of financial news data in recent years makes it increasingly challenging for investment analysts to extract valuable insights and perform analysis. We propose FactCheck in finance, a web-based news aggregator with deep learning models, to provide analysts with a holistic view of important financia...
Article
Full-text available
Traditional Recommender Systems (RS) use central servers to collect user data, compute user profiles and train global recommendation models. Central computation of RS models has great results in performance because the models are trained using all the available information and the full user profiles. However, centralised RS require users to share t...
Article
Full-text available
The end of 2020 and the beginning of 2021 was a challenging time for many countries in Europe, as the combination of colder weather, holiday celebrations, and the emergence of more transmissible virus variants conspired to create a perfect storm for virus transmission across the continent. At the same time lockdowns appeared to be less effective th...
Preprint
Full-text available
Recently, it has been proposed that fruitful synergies may exist between Deep Learning (DL) and Case Based Reasoning (CBR); that there are insights to be gained by applying CBR ideas to problems in DL (what could be called DeepCBR). In this paper, we report on a program of research that applies CBR solutions to the problem of Explainable AI (XAI) i...
Preprint
Full-text available
Climate change poses a major challenge to humanity, especially in its impact on agriculture, a challenge that a responsible AI should meet. In this paper, we examine a CBR system (PBI-CBR) designed to aid sustainable dairy farming by supporting grassland management, through accurate crop growth prediction. As climate changes, PBI-CBRs historical ca...
Preprint
Full-text available
In recent years, there has been an explosion of AI research on counterfactual explanations as a solution to the problem of eXplainable AI (XAI). These explanations seem to offer technical, psychological and legal benefits over other explanation techniques. We survey 100 distinct counterfactual explanation methods reported in the literature. This su...
Chapter
We propose a framework for fully decentralised machine learning and apply it to latent factor models for top-N recommendation. The training data in a decentralised learning setting is distributed across multiple agents, who jointly optimise a common global objective function (the loss function). Here, in contrast to the client-server architecture o...
Preprint
Counterfactual explanations provide a potentially significant solution to the Explainable AI (XAI) problem, but good, native counterfactuals have been shown to rarely occur in most datasets. Hence, the most popular methods generate synthetic counterfactuals using blind perturbation. However, such methods have several shortcomings: the resulting cou...
Chapter
In this work we generate user profiles from the raw activity data of over 12000 marathon runners. We demonstrate that these user profiles capture accurate representations of the fitness and training of a runner, and show that they are comparable to current methods used to predict marathon performance – many of which require many years of prior expe...
Preprint
Full-text available
Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year, and offer an interesting and challenging domain for artificial intelligence. However, in these highly sensitive domains, it is crucial to not only have a highly robust and accurate model, but be able to generate useful explanations to garner...
Chapter
Recently, a groundswell of research has identified the use of counterfactual explanations as a potentially significant solution to the Explainable AI (XAI) problem. It is argued that (i) technically, these counterfactual cases can be generated by permuting problem-features until a class-change is found, (ii) psychologically, they are much more caus...
Chapter
Training for the marathon, especially a first marathon, is always a challenge. Many runners struggle to find the right balance between their workouts and their recovery, often leading to sub-optimal performance on race-day or even injury during training. We describe and evaluate a novel case-based reasoning system to help marathon runners as they t...
Article
Full-text available
The wall is an iconic feature of the marathon. If runners hit the wall, usually around the 30km (20mi) mark, their pace slows dramatically, leaving them to struggle to the finish-line. While the physiology of the wall is reasonably well understood – a critical combination of fatigue and a lack of available fuel as the body’s glycogen stores become...
Article
Full-text available
Introduction: Critical speed (CS) represents the highest intensity at which a physiological steady state may be reached. The aim of this study was to evaluate whether estimations of CS obtained from raw training data can predict performance and pacing in marathons. Methods: We investigated running activities logged into an online fitness platfor...
Preprint
Recently, a groundswell of research has identified the use of counterfactual explanations as a potentially significant solution to the Explainable AI (XAI) problem. It is argued that (a) technically, these counterfactual cases can be generated by permuting problem-features until a class change is found, (b) psychologically, they are much more causa...
Conference Paper
Full-text available
The volatility forecasting task refers to predicting the amount of variability in the price of a financial asset over a certain period. It is an important mechanism for evaluating the risk associated with an asset and, as such, is of significant theoretical and practical importance in financial analysis. While classical approaches have framed this...
Article
Full-text available
Neural network-based recommendation algorithms have become the state-of-the-art in recommender systems and can achieve very high predictive accuracy. However, these models are usually considered as black boxes in terms of their interpretability due to the complex structure of their hidden layers. In this research work, we propose MP4Rec, a recommen...
Article
Full-text available
For the main part, when it comes to questions of retrieval, the focus of CBR research has been on the retrieval of cases from a repository of experience knowledge or case base. In this paper we consider a complementary retrieval issue, namely the retrieval of case bases themselves. We motivate this problem with reference to a deployed social web se...
Article
Full-text available
In this paper we focus on a multi-case case-based reasoning system to support users during collaborative search tasks. In particular we describe how repositories of search experiences/knowledge can be recommended to users at search time. These recommendations are evaluated using real-world search data.
Article
Full-text available
Much has been written about the need for a more personal-ized approach to search engines but until recently the mainstream players have been somewhat slow to respond. Nevertheless we now see concerted attempts by leading search engines to incorporate results that have originated from the searcher's social network (e.g. Twitter, Face-Book, Google+)...
Article
Full-text available
For the main part, when it comes to questions of retrieval, the focus of CBR research has been on the retrieval of cases from a repository of experience knowledge or case base. In this paper we consider a complementary retrieval issue, namely the retrieval of case bases themselves. We motivate this problem with reference to a deployed social web se...
Article
Full-text available
In this paper we focus on an approach to social search, HeyStaks that is designed to integrate with mainstream search engines such as Google, Yahoo and Bing. HeyStaks is motivated by the idea that Web search is an inherently social or collaborative activity. Heystaks users search as normal but benefit from collaboration features, allowing searchers...
Conference Paper
Starting their academic career can be overwhelming for many young people. Students are often presented with a variety of options within their programmes of study and making appropriate and informed decisions can be a challenge. Compared to many other areas in our everyday life, recommender systems remain underused in the academic setting. In this p...
Conference Paper
Full-text available
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 datasets to demonstrate improved rating pr...
Conference Paper
Full-text available
Recommender systems are omni-present in our every day lives, guiding us through the vast amount of information available. However, in the academic world, personalised recommendations are less prominent, leaving students to navigate through the typically large space of available courses and modules manually. Since it is crucial for students to make...
Conference Paper
The history of personalisation and recommender systems is, in large part, a web-tale: a story of sites and services that learn about users, in order to provide more tailored experiences. The rapid rise of mobile computing, combined with wearable sensors, and an increasingly connected IoT world, has begun to shift the potential for personalisation,...
Chapter
Traditionally, recommender systems have relied on user preference data (such as ratings) and product descriptions (such as meta-data) as primary sources of recommendation knowledge. More recently, new sources of recommendation knowledge in the form of social media information and other kinds of user-generated content have emerged as viable alternat...
Conference Paper
Full-text available
Collaborative filtering (CF) is a common recommendation approach that relies on user-item ratings. However, the natural sparsity of user-item rating data can be problematic in many domains and settings, limiting the ability to generate accurate predictions and effective recommendations. Moreover, in some CF approaches latent features are often used...
Conference Paper
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In this paper, we introduce a novel recommendation model, which harnesses a convolutional neural network to mine meaningful information from customer reviews, and integrates it with matrix factorization algorithm seamlessly. It is a valid method to improve the transparency of CF algorithms.
Conference Paper
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User-generated reviews are a plentiful source of user opinions and interests and can play an important role in a range of artificial intelligence contexts, particularly when it comes to recommender systems. In this paper, we describe how natural language processing and opinion mining techniques can be used to automatically mine useful recommendatio...
Conference Paper
Full-text available
In our increasingly algorithmic world, it is becoming more important, even compulsory, to support automated decisions with authentic and meaningful explanations. We extend recent work on the use of explanations by recommender systems. We review how compelling explanations can be created from the opinions mined from user-generated reviews by identif...
Conference Paper
Full-text available
The explosion of user-generated content, especially tweets, customer reviews, makes it possible to build sentiment lexicons automatically by harnessing the consistency between the content and its accompanying emotional signal, either explicitly or implicitly. In this work we describe novel techniques for automatically producing domain specific sent...
Conference Paper
Full-text available
E-commerce recommender systems seek out matches between customers and items in order to help customers discover more relevant and satisfying products and to increase the conversion rate of browsers to buyers. To do this, a recommender system must learn about the likes and dislikes of customers/users as well as the advantages and disadvantages (pros...
Conference Paper
Recommender systems have become a familiar part of our online experiences, suggesting movies to watch, music to listen to, and books to read, among other things. To make relevant suggestions, recommender systems need an accurate picture of our preferences and interests and sometimes even our friends and influencers. This information can be difficul...
Conference Paper
Full-text available
To help users discover relevant products and items recommender systems must learn about the likes and dislikes of users and the pros and cons of items. In this paper, we present a novel approach to building rich feature-based user profiles and item descriptions by mining user-generated reviews. We show how this information can be integrated into re...
Article
Full-text available
In the world of recommender systems, so-called content-based methods are an important approach that rely on the availability of detailed product or item descriptions to drive the recommendation process. For example, recommendations can be generated for a target user by selecting unseen products that are similar to the products that the target user...
Conference Paper
This paper describes a novel approach for generating explanations for recommender systems based on opinions in user-generated reviews. We show how these opinions can be used to construct helpful and compelling explanations at recommendation time. The explanation highlights how the pros and cons of a recommended item compares to alternative items. W...
Conference Paper
Explaining recommendations helps users to make better decisions. We describe a novel approach to explanation for recommender systems, one that drives the recommendation ranking process, while at the same time providing the user with useful insights into the reason why items have been recommended and the trade-offs they may need to consider when mak...
Article
Full-text available
Guest editor’s introduction: special issue on case-based reasoning David B. Leake, Barry Smyth and Rosina Weber This Special Issue contains three articles providing samples of current case-based reasoning (CBR) research. Case-based reasoning systems perform problem-solving and interpretation based on a library of prior cases, called the case base....
Conference Paper
In this paper we share our experiences of working with a real-time news recommendation framework with real-world user and data.
Patent
Full-text available
A system, method, and computer program product are provided for determining information associated with an extracted portion of content. In use, a user is identified. Additionally, content generated by the user is identified. Additionally, a portion of the content is extracted. Further, information associated with the extracted portion of the conte...
Chapter
Modern web search engines have come to dominate how millions of people find the information that they are looking for online. While the sheer scale and success of the leading search engines is a testimony to the scientific and engineering progress that has been made over the last two decades, mainstream search is not without its challenges. Mainstr...