Financial news recommendation based on graph embeddings
Authors: Ren, Jiangtao; Long, Jiawei; Xu, Zhikang
Journal: Decision Support Systems (2019)
DOI: 10.1016/j.dss.2019.113115
Most of the existing methods are not enough for securities companies to make their decisions on recommending the most suitable financial news to a specific user. On the one hand, such news articles often contain externa knowledge related to companies and stocks. On the other hand, it is important for financial news re commendations to dynamically measure users' interests since people are usually interested in multiple specific concepts, companies, stocks and industry categories. To address the above challenges, we start by building a heterogeneous graph consisting of users, news, companies, concepts, and industry categories. Then, the graph embeddings of the nodes are generated using node2vec, and user-news relatedness can be computed based on them. Since financial news articles are time-sensitive, we propose an incremental method for alleviating the computational eficiency problem. The combination of a node2vec-based recommendation method and the incremental method can achieve a good balance between time eficiency and recommendation accuracy in the financial news recommendation task. Our methods are evaluated on a real-world dataset from a Chinese securities company, are shown to…