Deep Learning on Knowledge Graph for Recommender System: A Survey
It provides a comprehensive overview for researchers and practitioners in AI and recommendation systems, but it is incremental as it surveys existing work rather than introducing new methods.
This survey paper reviews the use of knowledge graphs and Graph Neural Networks to enhance recommender systems by encoding high-order relations and extracting object characteristics, summarizing state-of-the-art frameworks, datasets, and open-source codes.
Recent advances in research have demonstrated the effectiveness of knowledge graphs (KG) in providing valuable external knowledge to improve recommendation systems (RS). A knowledge graph is capable of encoding high-order relations that connect two objects with one or multiple related attributes. With the help of the emerging Graph Neural Networks (GNN), it is possible to extract both object characteristics and relations from KG, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of the GNN-based knowledge-aware deep recommender systems. Specifically, we discuss the state-of-the-art frameworks with a focus on their core component, i.e., the graph embedding module, and how they address practical recommendation issues such as scalability, cold-start and so on. We further summarize the commonly-used benchmark datasets, evaluation metrics as well as open-source codes. Finally, we conclude the survey and propose potential research directions in this rapidly growing field.