8 citations · 35 across the 13 of their papers we have counts for
13 papers
From Raw IDs to Semantic Planning: How Recommender Systems Utilize Information at Scale
Changhong Jin, Shiqiu Yang, Roger Zhe Li +8
The evolution of recommender systems can be explored by asking how they utilize information at scale. Throughout most of the historical period under consideration during the past t…
Industry Classification Using a Novel Financial Time-Series Case Representation
Rian Dolphin, Barry Smyth, Ruihai Dong
The financial domain has proven to be a fertile source of challenging machine learning problems across a variety of tasks including prediction, clustering, and classification. Rese…
Item Graph Convolution Collaborative Filtering for Inductive Recommendations
Edoardo D'Amico, Khalil Muhammad, Elias Tragos +3
Graph Convolutional Networks (GCN) have been recently employed as core component in the construction of recommender system algorithms, interpreting user-item interactions as the ed…
A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets
Rian Dolphin, Barry Smyth, Ruihai Dong
Industry classification schemes provide a taxonomy for segmenting companies based on their business activities. They are relied upon in industry and academia as an integral compone…
Stock Embeddings: Learning Distributed Representations for Financial Assets
Rian Dolphin, Barry Smyth, Ruihai Dong
Identifying meaningful relationships between the price movements of financial assets is a challenging but important problem in a variety of financial applications. However with rec…
NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting
Linyi Yang, Jiazheng Li, Ruihai Dong +2
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 improvement…
Investigating Health-Aware Smart-Nudging with Machine Learning to Help People Pursue Healthier Eating-Habits
Mansura A Khan, Khalil Muhammad, Barry Smyth +1
Food-choices and eating-habits directly contribute to our long-term health. This makes the food recommender system a potential tool to address the global crisis of obesity and maln…
Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities
Rian Dolphin, Barry Smyth, Yang Xu +1
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…
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis
Linyi Yang, Jiazheng Li, Pádraig Cunningham +3
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 robustnes…
Fact Check: Analyzing Financial Events from Multilingual News Sources
Linyi Yang, Tin Lok James Ng, Barry Smyth +1
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 an…
Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future
Mohammed Temraz, Eoin Kenny, Elodie Ruelle +3
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 (…
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 +1
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 te…
A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations
Barry Smyth, Mark T Keane
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…