A Survey on Knowledge Graph-Based Recommender Systems

By Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He. IEEE Transactions on Knowledge and Data Engineering, 2020

1,001 citations recorded by the source corpus.

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 problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned 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 group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/b4cc53835ecee68bc37db1fbb1631573a3900906

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Yong Li
4. Meng Wang
5. Jure Leskovec
6. Wayne Xin Zhao
7. Chen Gao
8. Le Wu

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Yong Li
5. Chen Gao
6. Le Wu
7. Wayne Xin Zhao
8. Yupeng Hou

Citations in the corpus

• Graph Neural Networks in Recommender Systems: A Survey (2020, neutral): “, multi-type entities and multi-type relations [13].”
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, background): “• There are other surveys on specific topics of recommendation, such as Reference [175] for session-based recommendation, References [30, 59] for knowledge-graph based recommendation, Reference [26] for explainable recommendation, Reference [191] for news recommendation.”
• Knowledge Graphs: Opportunities and Challenges (2023, background): “The first task typically adopts link prediction techniques to generate triplets and then assigns the triplets plausibility scores (Ji et al. 2021).”
• Large Language Models are Zero-Shot Rankers for Recommender Systems (2023, background): “Secondly, these models are essentially “ narrow experts ”, lacking more comprehensive knowledge in solving complicated recommendation tasks that rely on background or commonsense knowledge [11].”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “domain recommendation [8], explainable recommendation [11], knowledge graph-enhanced recommendation [12], sequential recommendation [13], [14], and session-based”
• OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs (2021, background): “They provide rich structured information about many entities, aiding a variety of knowledgeintensive downstream applications such as information retrieval, question answering (Singhal, 2012), and recommender systems (Guo et al., 2020).”