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Barry Smyth

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28 papers
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28

IJCAI Conference 2023 Conference Paper

Keeping People Active and Healthy at Home Using a Reinforcement Learning-based Fitness Recommendation Framework

  • Elias Tragos
  • Diarmuid O'Reilly-Morgan
  • James Geraci
  • Bichen Shi
  • Barry Smyth
  • Cailbhe Doherty
  • Aonghus Lawlor
  • Neil Hurley

Recent years have seen a rise in smartphone applications promoting health and well being. We argue that there is a large and unexplored ground within the field of recommender systems (RS) for applications that promote good personal health. During the COVID-19 pandemic, with gyms being closed, the demand for at-home fitness apps increased as users wished to maintain their physical and mental health. However, maintaining long-term user engagement with fitness applications has proved a difficult task. Personalisation of the app recommendations that change over time can be a key factor for maintaining high user engagement. In this work we propose a reinforcement learning (RL) based framework for recommending sequences of body-weight exercises to home users over a mobile application interface. The framework employs a user simulator, tuned to feedback a weighted sum of realistic workout rewards, and trains a neural network model to maximise the expected reward over generated exercise sequences. We evaluate our framework within the context of a large 15 week live user trial, showing that an RL based approach leads to a significant increase in user engagement compared to a baseline recommendation algorithm.

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AAAI Conference 2022 Conference Paper

NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-Task Financial Forecasting

  • Linyi Yang
  • Jiazheng Li
  • Ruihai Dong
  • Yue Zhang
  • Barry Smyth

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 financial statements. Earnings conference call data, including text and audio, is an important source of unstructured data that has been used for various prediction tasks using deep earning and related approaches. However, current deep learningbased methods are limited in the way that they deal with numeric data; numbers are typically treated as plain-text tokens without taking advantage of their underlying numeric structure. This paper describes a numeric-oriented hierarchical transformer model (NumHTML) to predict stock returns, and financial risk using multi-modal aligned earnings calls data by taking advantage of the different categories of numbers (monetary, temporal, percentages etc.) and their magnitude. We present the results of a comprehensive evaluation of NumHTML against several state-of-the-art baselines using a real-world publicly available dataset. The results indicate that NumHTML significantly outperforms the current stateof-the-art across a variety of evaluation metrics and that it has the potential to offer significant financial gains in a practical trading context.

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IJCAI Conference 2021 Conference Paper

If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques

  • Mark T. Keane
  • Eoin M. Kenny
  • Eoin Delaney
  • Barry Smyth

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 survey addresses the extent to which these methods have been adequately evaluated, both psychologically and computationally, and quantifies the shortfalls occurring. For instance, only 21% of these methods have been user tested. Five key deficits in the evaluation of these methods are detailed and a roadmap, with standardised benchmark evaluations, is proposed to resolve the issues arising; issues, that currently effectively block scientific progress in this field.

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AAAI Conference 2019 Conference Paper

Recommender Systems: A Healthy Obsession

  • Barry Smyth

We propose endurance sports as a rich and novel domain for recommender systems and machine learning research. As sports like marathon running, triathlons, and mountain biking become more and more popular among recreational athletes, there exists a growing opportunity to develop solutions to a number of interesting prediction, classification, and recommendation challenges, to better support the complex training and competition needs of athletes. Such solutions have the potential to improve the health and well-being of large populations of users, by promoting and optimising exercise as part of a productive and healthy lifestyle.

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IJCAI Conference 2018 Conference Paper

Marathon Race Planning: A Case-Based Reasoning Approach

  • Barry Smyth
  • PADRAIG CUNNINGHAM

We describe and evaluate a novel application of case-based reasoning to help marathon runners to achieve a personal best by: (a) predicting a challenging, but realistic race-time; and (b) recommending a race-plan to achieve this time.

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IJCAI Conference 2017 Conference Paper

User-Based Opinion-based Recommendation

  • Ruihai Dong
  • Barry Smyth

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 recommendation knowledge from user generated reviews and how this information can be used by recommender systems in a number of classical settings.

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IJCAI Conference 2013 Conference Paper

Topic Extraction from Online Reviews for Classification and Recommendation

  • Ruihai Dong
  • Markus Schaal
  • Michael P. O'Mahony
  • Barry Smyth

Automatically identifying informative reviews is increasingly important given the rapid growth of user generated reviews on sites like Amazon and TripAdvisor. In this paper, we describe and evaluate techniques for identifying and recommending helpful product reviews using a combination of review features, including topical and sentiment information, mined from a review corpus.

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TIST Journal 2011 Journal Article

A Case Study of Collaboration and Reputation in Social Web Search

  • Kevin McNally
  • Michael P. O’Mahony
  • Maurice Coyle
  • Peter Briggs
  • Barry Smyth

Although collaborative searching is not supported by mainstream search engines, recent research has highlighted the inherently collaborative nature of many Web search tasks. In this article, we describe HeyStaks, a collaborative Web search framework that is designed to complement mainstream search engines. At search time, HeyStaks learns from the search activities of other users and leverages this information to generate recommendations based on results that others have found relevant for similar searches. The key contribution of this article is to extend the HeyStaks social search model by considering the search expertise, or reputation, of HeyStaks users and using this information to enhance the result recommendation process. In particular, we propose a reputation model for HeyStaks users that utilise the implicit collaboration events that take place between users as recommendations are made and selected. We describe a live-user trial of HeyStaks that demonstrates the relevance of its core recommendations and the ability of the reputation model to further improve recommendation quality. Our findings indicate that incorporating reputation into the recommendation process further improves the relevance of HeyStaks recommendations by up to 40%.

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IJCAI Conference 2007 Conference Paper

  • John O'Donovan
  • Barry Smyth
  • Vesile Evrim
  • Dennis McLeod

Buyers and sellers in online auctions are faced with the task of deciding who to entrust their business to based on a very limited amount of information. Current trust ratings on eBay average over 99 percent positive and are presented as a single number on a user profile. This paper presents a system capable of extracting valuable negative information from the wealth of feedback comments on eBay, computing personalized and feature-based trust and presenting this information graphically.

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ECAI Conference 2006 Conference Paper

ECUE: A Spam Filter that Uses Machine Leaming to Track Concept Drift

  • Sarah Jane Delany
  • Padraig Cunningham
  • Barry Smyth

While text classification has been identified for some time as a promising application area for Artificial Intelligence, so far few deployed applications have been described. In this paper we present a spam filtering system that uses example-based machine learning techniques to train a classifier from examples of spam and legitimate email. This approach has the advantage that it can personalise to the specifics of the user's filtering preferences. This classifier can also automatically adjust over time to account for the changing nature of spam (and indeed changes in the profile of legitimate email). A significant software engineering challenge in developing this system was to ensure that it could interoperate with existing email systems to allow easy managment of the training data over time. This system has been deployed and evaluated over an extended period and the results of this evaluation are presented here.

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IJCAI Conference 2005 Conference Paper

A Live-User Evaluation of Collaborative Web Search

  • Barry Smyth
  • Evelyn Balfe
  • Oisin Boydell
  • Keith Bradley
  • Peter Briggs
  • Maurice Coyle
  • Jill

Collaborative Web search exploits repetition and regularity within the query-space of a community of like-minded individuals in order to improve the quality of search results. In short, search results that have been judged to be relevant for past queries are promoted in response to similar queries that occur in the future. In this paper we present the results of a large-scale evaluation of this approach, in a corporate Web search scenario, which shows that significant benefits are available to its users.

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IJCAI Conference 2005 Conference Paper

A Study of Selection Noise in Collaborative Web Search

  • Oisín Boydell
  • Barry Smyth
  • Cathal Gurrin
  • Alan F

Collaborative Web search uses the past search behaviour (queries and selections) of a community of users to promote search results that are relevant to the community. The extent to which these promotions are likely to be relevant depends on how reliably past search behaviour can be captured. We consider this issue by analysing the results of collaborative Web search in circumstances where the behaviour of searchers is unreliable.

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KER Journal 2005 Journal Article

Case-based recommender systems

  • Derek Bridge
  • MEHMET H. GÖKER
  • LORRAINE McGINTY
  • Barry Smyth

We describe recommender systems and especially case-based recommender systems. We define a framework in which these systems can be understood. The framework contrasts collaborative with case-based, reactive with proactive, single-shot with conversational, and asking with proposing. Within this framework, we review a selection of papers from the case-based recommender systems literature, covering the development of these systems over the last ten years.

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AAAI Conference 2005 Conference Paper

On the Evaluation of Dynamic Critiquing: A Large-Scale User Study

  • Kevin McCarthy
  • Barry Smyth

Critiquing is an important form of feedback in conversational recommender systems. However, in these systems the user is usually limited to critiquing a single product feature at a time. Recently dynamic critiquing has been proposed to address this shortcoming, by automatically generating compound critiques over multiple features that may be presented to the user at recommendation time. To date a number of different versions of dynamic critiquing have been evaluated in isolation, and with reference to artificial users. In this paper we bring together the main flavors of dynamic critiquing and perform a large-scale comparative evaluation as part of an extensive real-user trial. This evaluation reveals some interesting facts about the way real users interact with critique-based recommenders.

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KER Journal 2005 Journal Article

Retrieval, reuse, revision and retention in case-based reasoning

  • Ramon Lopez de Mantaras
  • David McSherry
  • Derek Bridge
  • David Leake
  • Barry Smyth
  • Susan Craw
  • Boi Faltings
  • Mary Lou Maher

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision and retention.

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IJCAI Conference 2003 Conference Paper

Collaborative Web Search

  • Barry Smyth
  • Evelyn Balfe
  • Peter Briggs
  • Maurice Coyle
  • Jill Freyne

Web search engines struggle to satisfy the needs of Web users. Users are notoriously poor at representing their needs in the form of a query, and search engines are poor at responding to vague queries. However progress has been made by introducing context into the search process. In this paper we describe and evaluate a novel approach to using context in Web search that adapts a generic search engine for the needs of a specialist community of users. This collaborative search method enjoys significant performance benefits and avoids the privacy and security concerns that are commonly associated with related personalization research.

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IJCAI Conference 2003 Conference Paper

Explicit vs Implicit Profiling - A Case-Study in Electronic Programme Guides

  • Derry O'Sullivan
  • Barry Smyth
  • David Wilson

In this paper, we evaluate the use of implicit interest indicators as the basis for user profiling in the Digital TV domain. Research in more traditional domains, such as Web browsing or Usenet News, indicates that some implicit interest indicators (e. g. , read-time and mouse movements) are capable of serving as alternative to explicit profile information such as user ratings. Consequently, the key question we wish to answer relates to the type of implicit indicators that can be identified within the DTV domain and the extent to which they can accurately reflect a user's true preferences.

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IJCAI Conference 2003 Conference Paper

The Power of Suggestion

  • Barry Smyth
  • LORRAINE McGINTY

User feedback is vital in many recommender systems to help guide the search for good recommendations. Preference-based feedback (e. g. "Show me more like item A ") is an inherently ambiguous form of feedback with a limited ability to guide the recommendation process, and for this reason it is usually avoided. Nevertheless we believe that certain domains demand the use of preference-based feedback. As such, we describe and evaluate a flexible recommendation strategy that has the potential to improve the performance of case-based recommenders that rely on preference-based feedback.

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AIJ Journal 1998 Journal Article

Adaptation-guided retrieval: questioning the similarity assumption in reasoning

  • Barry Smyth
  • Mark T. Keane

One of the major assumptions in Artificial Intelligence is that similar experiences can guide future reasoning, problem solving and learning; what we will call, the similarity assumption. The similarity assumption is used in problem solving and reasoning systems when target problems are dealt with by resorting to a previous situation with common conceptual features. In this article, we question this assumption in the context of case-based reasoning (CBR). In CBR, the similarity assumption plays a central role when new problems are solved, by retrieving similar cases and adapting their solutions. The success of any CBR system is contingent on the retrieval of a case that can be successfully reused to solve the target problem. We show that it is often unwarranted to assume that the most similar case is also the most appropriate from a reuse perspective. We argue that similarity must be augmented by deeper, adaptation knowledge about whether a case can be easily modified to fit a target problem. We implement this idea in a new technique, called adaptation-guided retrieval (AGR), which provides a direct link between retrieval similarity and adaptation needs. This technique uses specially formulated adaptation knowledge, which, during retrieval, facilitates the computation of a precise measure of a case's adaptation requirements. In closing, we assess the broader implications of AGR and argue that it is just one of a growing number of methods that seek to overcome the limitations of the traditional similarity assumption in an effort to deliver more sophisticated and scalable reasoning systems.

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IJCAI Conference 1995 Conference Paper

Remembering To Forget: A Competence-Preserving Case Deletion Policy for Case-Based Reasoning Systems

  • Barry Smyth
  • Mark T. Keane

The utility problem occurs when the cost associated with searching for relevant knowledge outweighs the benefit of applying this knowledge. One common machine learning strategy for coping with this problem ensures that stored knowledge is genuinely useful, deleting any structures that do not contribute to performance in a positive sense, and essentially limiting the size of the knowledge-base. We will examine this deletion strategy in the context of casebased reasoning (CBR) systems. In CBR the impact of the utility problem is very much dependant on the size and growth of the case-base; larger case-bases mean more expensive retrieval stages, an expensive overhead in CBR systems. Traditional deletion strategies will keep performance in check (and thereby control the classical utility problem) but they may cause problems for CBR system competence. This effect is demonstrated experimentally and in reply two new deletion strategies are proposed that can take both competence and performance into consideration during deletion.

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