Personalized Recommendation Algorithm for Financial News Based on Knowledge Graph

Abstract

Personalized news information recommendation can attract a large number of highly sticky users because of its ability to effectively capture user interests and provide high-quality recommendation services.Knowledge graph represents the relationships between things in the entity-relation-entity form, which enables the learning of richer features and semantic information.To increase the quality of personalized recommendation of news in the financial field, this paper proposes a personalized recommendation algorithm, KHA-CNN, based on knowledge graph.Combined with the knowledge graph in the financial industry, a knowledge-based convolutional neural network and the hierarchical attention mechanism are used to obtain the feature representation of news texts, and to learn the features of the complex behavior data of users.Experimental results on real data sets show that compared with Random Forest, DKN, and ATRank-like algorithms, the KHA-CNN algorithm increases the F1 score by 2.6 percentange points, and the AUC indicator by 1.5 percentange points.