A Survey on Knowledge Graph-Based Recommender Systems

TL;DR

This survey reviews recent advances in knowledge graph-based recommender systems, highlighting their methods, datasets, and potential future research directions to improve recommendation accuracy and explainability.

Contribution

It provides a comprehensive overview of algorithms and datasets in knowledge graph-based recommender systems, and suggests future research directions.

Findings

Knowledge graphs improve recommendation accuracy and explainability.

Recent algorithms effectively utilize knowledge graphs for personalized recommendations.

The survey identifies key datasets and challenges in the field.

Abstract

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold start. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the abovementioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field and summarize them from two perspectives. On the one hand, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. On the other hand, we introduce datasets used in these works. Finally, we propose several potential research directions in this field.