# RecKG: Knowledge Graph for Recommender Systems

arXiv: https://arxiv.org/abs/2501.03598 · HTML: https://mlsanity.com/paper/2501.03598/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 7 Jan 2025.

## Builds on (most cited first, 23 of 23)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [KGAT: Knowledge Graph Attention Network for Recommendation](https://mlsanity.com/paper/1905.07854/) (2019)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018)
- [Knowledge Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1904.12575/) (2019)
- [Learning Intents behind Interactions with Knowledge Graph for Recommendation](https://mlsanity.com/paper/2102.07057/) (2021)
- [Knowledge Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2205.00976/) (2022)
- [Jointly Learning Explainable Rules for Recommendation with Knowledge Graph](https://mlsanity.com/paper/1903.03714/) (2019)
- [Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System](https://mlsanity.com/paper/2204.08807/) (2022)
- [Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network](https://mlsanity.com/paper/2110.04002/) (2021)
- [Knowledge Graph Self-Supervised Rationalization for Recommendation](https://mlsanity.com/paper/2307.02759/) (2023)
- [Explainable Reasoning over Knowledge Graphs for Recommendation](https://mlsanity.com/paper/1811.04540/) (2018)
- [A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations](https://mlsanity.com/paper/2011.10187/) (2020)
- [Price-aware Recommendation with Graph Convolutional Networks](https://mlsanity.com/paper/2003.03975/) (2020)
- [Decentralized Collaborative Learning Framework for Next POI Recommendation](https://mlsanity.com/paper/2204.06516/) (2022)
- [Multi-Behavior Graph Neural Networks for Recommender System](https://mlsanity.com/paper/2302.08678/) (2023)
- [Knowledge-refined Denoising Network for Robust Recommendation](https://mlsanity.com/paper/2304.14987/) (2023)
- [Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison](https://mlsanity.com/paper/2203.14205/) (2022)
- [Link Climate: An Interoperable Knowledge Graph Platform for Climate Data](https://mlsanity.com/paper/2210.16050/) (2022)
- [EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph for Smart Transportation System](https://mlsanity.com/paper/2304.04893/) (2023)
- [Personalized Next Point-of-Interest Recommendation via Latent Behavior Patterns Inference](https://mlsanity.com/paper/1805.06316/) (2018)
- [Analyzing a Knowledge Graph of Industry4.0 Standards](https://mlsanity.com/paper/2107.01910/) (2021)

## Built on by (most cited first, 4 of 4)

- [AR-Med: Automated Relevance Enhancement in Medical Search via LLM-Driven Information Augmentation](https://mlsanity.com/paper/2512.03737/) (2025)
- [Construction of Knowledge Graph based on Language Model](https://mlsanity.com/paper/2604.19137/) (2026)
- [Inferring Sensitive Attributes from Knowledge Graph Embeddings: Attack and Defense Strategies](https://mlsanity.com/paper/2605.19644/) (2026)
- [Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion](https://mlsanity.com/paper/2606.05639/) (2026)

## Abstract (arXiv)

Knowledge graphs have proven successful in integrating heterogeneous data across various domains. However, there remains a noticeable dearth of research on their seamless integration among heterogeneous recommender systems, despite knowledge graph-based recommender systems garnering extensive research attention. This study aims to fill this gap by proposing RecKG, a standardized knowledge graph for recommender systems. RecKG ensures the consistent representation of entities across different datasets, accommodating diverse attribute types for effective data integration. Through a meticulous examination of various recommender system datasets, we select attributes for RecKG, ensuring standardized formatting through consistent naming conventions. By these characteristics, RecKG can seamlessly integrate heterogeneous data sources, enabling the discovery of additional semantic information within the integrated knowledge graph. We apply RecKG to standardize real-world datasets, subsequently developing an application for RecKG using a graph database. Finally, we validate RecKG's achievement in interoperability through a qualitative evaluation between RecKG and other studies.