40 citations · 91 across the 19 of their papers we have counts for
19 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…
Contrastive Learning of Asset Embeddings from Financial Time Series
Rian Dolphin, Barry Smyth, Ruihai Dong
Representation learning has emerged as a powerful paradigm for extracting valuable latent features from complex, high-dimensional data. In financial domains, learning informative r…
Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations
Boming Yang, Dairui Liu, Toyotaro Suzumura +2
Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems. Most recent works primarily focus on using ad…
Enhancing Topic Extraction in Recommender Systems with Entropy Regularization
Xuefei Jiang, Dairui Liu, Ruihai Dong
In recent years, many recommender systems have utilized textual data for topic extraction to enhance interpretability. However, our findings reveal a noticeable deficiency in the c…
Topic-Centric Explanations for News Recommendation
Dairui Liu, Derek Greene, Irene Li +2
News recommender systems (NRS) have been widely applied for online news websites to help users find relevant articles based on their interests. Recent methods have demonstrated con…
Learning to Generalize for Cross-domain QA
Yingjie Niu, Linyi Yang, Ruihai Dong +1
There have been growing concerns regarding the out-of-domain generalization ability of natural language processing (NLP) models, particularly in question-answering (QA) tasks. Curr…
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…
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…
A Novel Perspective to Look At Attention: Bi-level Attention-based Explainable Topic Modeling for News Classification
Dairui Liu, Derek Greene, Ruihai Dong
Many recent deep learning-based solutions have widely adopted the attention-based mechanism in various tasks of the NLP discipline. However, the inherent characteristics of deep le…
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…
Learning to Compose Diversified Prompts for Image Emotion Classification
Sinuo Deng, Lifang Wu, Ge Shi +3
Contrastive Language-Image Pre-training (CLIP) represents the latest incarnation of pre-trained vision-language models. Although CLIP has recently shown its superior power on a wid…
Pseudo-labelling Enhanced Media Bias Detection
Qin Ruan, Brian Mac Namee, Ruihai Dong
Leveraging unlabelled data through weak or distant supervision is a compelling approach to developing more effective text classification models. This paper proposes a simple but ef…
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…
LiGCN: Label-interpretable Graph Convolutional Networks for Multi-label Text Classification
Irene Li, Aosong Feng, Hao Wu +3
Multi-label text classification (MLTC) is an attractive and challenging task in natural language processing (NLP). Compared with single-label text classification, MLTC has a wider…
Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification
Linyi Yang, Eoin M. Kenny, Tin Lok James Ng +3
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 intellige…
Explainable Text-Driven Neural Network for Stock Prediction
Linyi Yang, Zheng Zhang, Su Xiong +4
It has been shown that financial news leads to the fluctuation of stock prices. However, previous work on news-driven financial market prediction focused only on predicting stock p…