Predictions of stock and foreign exchange (Forex) have always been a hot and profitable
area of study. Deep learning applications have been proven to yield better accuracy and …

Stock market prediction has been a classical yet challenging problem, with the attention from
both economists and computer scientists. With the purpose of building an effective prediction …

Graph Convolution Network (GCN) has become new state-of-the-art for collaborative
filtering. Nevertheless, the reasons of its effectiveness for recommendation are not well …

Stock movement prediction is an important field of study that can help market traders make
better trading decisions and earn more profit. The fusion of text from social media platforms …

Incorporating knowledge graph (KG) into recommender system is promising in improving the
recommendation accuracy and explainability. However, existing methods largely assume …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Learning user representations based on historical behaviors lies at the core of modern
recommender systems. Recent advances in sequential recommenders have convincingly …

Stock ranking is a significant and challenging problem. In recent years, the use of multi-view
data, such as price and tweet, for stock ranking has gained considerable attention in the …

This paper contributes a new machine learning solution for stock movement prediction,
which aims to predict whether the price of a stock will be up or down in the near future. The …

With multiple components and relations, financial data are often presented as graph data,
since it could represent both the individual features and the complicated relations. Due to …