# Deep Learning on Knowledge Graph for Recommender System: A Survey

arXiv: https://arxiv.org/abs/2004.00387 · HTML: https://mlsanity.com/paper/2004.00387/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 19 of 19)

- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [Graph Learning based Recommender Systems: A Review](https://mlsanity.com/paper/2105.06339/) (2021)
- [LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations](https://mlsanity.com/paper/2402.09617/) (2024)
- [Recent Developments in Recommender Systems: A Survey](https://mlsanity.com/paper/2306.12680/) (2023)
- [GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers](https://mlsanity.com/paper/2210.17281/) (2022)
- [Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation](https://mlsanity.com/paper/2111.02100/) (2021)
- [Recommender systems based on graph embedding techniques: A comprehensive review](https://mlsanity.com/paper/2109.09587/) (2021)
- [Multimodal Metadata Assignment for Cultural Heritage Artifacts](https://mlsanity.com/paper/2406.00423/) (2024)
- [HRGraph: Leveraging LLMs for HR Data Knowledge Graphs with Information Propagation-based Job Recommendation](https://mlsanity.com/paper/2408.13521/) (2024)
- [User Embedding based Neighborhood Aggregation Method for Inductive Recommendation](https://mlsanity.com/paper/2102.07575/) (2021)
- [Attention-Based Recommendation On Graphs](https://mlsanity.com/paper/2201.05499/) (2022)
- [Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion?](https://mlsanity.com/paper/2311.09109/) (2023)
- [KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?](https://mlsanity.com/paper/2404.03164/) (2024)
- [Debiasing Graph Representation Learning based on Information Bottleneck](https://mlsanity.com/paper/2409.01367/) (2024)
- [Knowledge Graphs and Machine Learning in biased C4I applications](https://mlsanity.com/paper/2106.09258/) (2021)
- [Unified Interpretation of Smoothing Methods for Negative Sampling Loss Functions in Knowledge Graph Embedding](https://mlsanity.com/paper/2407.04251/) (2024)
- [Enhancing Cross-Market Recommendation System with Graph Isomorphism Networks: A Novel Approach to Personalized User Experience](https://mlsanity.com/paper/2409.07850/) (2024)
- [A Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System for Advertisement Retrieval and Personalization](https://mlsanity.com/paper/2601.00833/) (2025)
- [Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey](https://mlsanity.com/paper/2607.09666/) (2026)

## Abstract (arXiv)

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.