Deep Learning on Knowledge Graph for Recommender System: A Survey

TL;DR

This survey reviews how graph neural networks applied to knowledge graphs enhance recommender systems, discussing frameworks, challenges, datasets, and future research directions.

Contribution

It provides a comprehensive overview of GNN-based knowledge-aware recommender systems, highlighting core components, challenges, and open research areas.

Findings

Summarizes state-of-the-art GNN frameworks for recommendation

Analyzes key challenges like scalability and cold-start

Provides benchmark datasets and evaluation metrics

Abstract

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.