Knowledge enhanced prompt learning framework for financial news recommendation

Abstract

The aim of financial news recommendation systems is to deliver personalized and timely financial information. Traditional methods face challenges, including the complexity of financial news, which requires stock-related external knowledge and accounts for users' interests in various stocks, industries, and concepts. Additionally, the financial domain's timeliness necessitates adaptable recommender systems, especially in few-shot and cold-start scenarios. To address these challenges, we propose a knowledge-enhanced prompt learning framework for financial news recommendation (FNRKPL). FNRKPL incorporates a financial news knowledge graph and transforms triple information into prompt language to strengthen the recommendation model's knowledge base. Personalized prompt templates are designed to account for users' topic preferences and sentiment tendencies, integrating knowledge, topic, and sentiment prompts. Furthermore, a knowledge-enhanced prompt learning mechanism enhances the model's generalization and adaptability in few-shot and cold-start scenarios. Extensive experiments on real-world corporate datasets validate FNRKPL's effectiveness in both data-rich and resource-poor conditions.