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Found 19 papers, 7 papers with code
Contrastive Learning of Asset Embeddings from Financial Time Series
In each case our novel approaches significantly outperform existing baselines highlighting the potential for contrastive learning to capture meaningful and actionable relationships in financial data.
Facilitating Interdisciplinary Knowledge Transfer with Research Paper Recommender Systems
In the extensive recommender systems literature, novelty and diversity have been identified as key properties of useful recommendations.
Industry Classification Using a Novel Financial Time-Series Case Representation
We discuss why time-series data can present some significant representational challenges for conventional case-based reasoning approaches, and in response, we propose a novel representation based on stock returns embeddings, which can be readily calculated from raw stock returns data.
Item Graph Convolution Collaborative Filtering for Inductive Recommendations
We propose the construction of an item-item graph through a weighted projection of the bipartite interaction network and to employ convolution to inject higher order associations into item embeddings, while constructing user representations as weighted sums of the items with which they have interacted.
A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets
Industry classification schemes provide a taxonomy for segmenting companies based on their business activities.
Stock Embeddings: Learning Distributed Representations for Financial Assets
Identifying meaningful relationships between the price movements of financial assets is a challenging but important problem in a variety of financial applications.
NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting
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.
Investigating Health-Aware Smart-Nudging with Machine Learning to Help People Pursue Healthier Eating-Habits
To develop smart nudging for promoting healthier food choices, we combined Machine Learning and RS technology with food-healthiness guidelines from recognized health organizations, such as the World Health Organization, Food Standards Agency, and the National Health Service United Kingdom.
Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities
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.
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis
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.
Fact Check: Analyzing Financial Events from Multilingual News Sources
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.
Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based Reasoning Pairings for Explanation and Data Augmentation
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).
Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future
Climate change poses a major challenge to humanity, especially in its impact on agriculture, a challenge that a responsible AI should meet.
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques
In recent years, there has been an explosion of AI research on counterfactual explanations as a solution to the problem of eXplainable AI (XAI).
A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations
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.
Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification
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.
Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)
Recently, a groundswell of research has identified the use of counterfactual explanations as a potentially significant solution to the Explainable AI (XAI) problem.
Personalized, Health-Aware Recipe Recommendation: An Ensemble Topic Modeling Based Approach
This paper proposes two different EnsTM based and one Hybrid EnsTM based recommenders.
RARD II: The 94 Million Related-Article Recommendation Dataset
The main contribution of this paper is to introduce and describe a new recommender-systems dataset (RARD II).
