# HAKG: Hierarchy-Aware Knowledge Gated Network for Recommendation

arXiv: https://arxiv.org/abs/2204.04959 · HTML: https://mlsanity.com/paper/2204.04959/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2022.

## Built on by (most cited first, 16 of 16)

- [Knowledge-grounded Natural Language Recommendation Explanation](https://mlsanity.com/paper/2308.15813/) (2023)
- [WorldDB: A Vector Graph-of-Worlds Memory Engine with Ontology-Aware Write-Time Reconciliation](https://mlsanity.com/paper/2604.18478/) (2026)
- [GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning](https://mlsanity.com/paper/2304.12537/) (2023)
- [Towards Explainable Collaborative Filtering with Taste Clusters Learning](https://mlsanity.com/paper/2304.13937/) (2023)
- [Probing Pretrained Language Models with Hierarchy Properties](https://mlsanity.com/paper/2312.09670/) (2023)
- [Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2403.04521/) (2024)
- [Lorentz Equivariant Model for Knowledge-Enhanced Hyperbolic Collaborative Filtering](https://mlsanity.com/paper/2302.04545/) (2023)
- [Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks](https://mlsanity.com/paper/2401.06557/) (2024)
- [InBox: Recommendation with Knowledge Graph using Interest Box Embedding](https://mlsanity.com/paper/2403.12649/) (2024)
- [KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?](https://mlsanity.com/paper/2404.03164/) (2024)
- [Knowledge Graph Pruning for Recommendation](https://mlsanity.com/paper/2405.11531/) (2024)
- [Breaking Information Cocoons: A Hyperbolic Framework for Balancing Exploration and Exploitation in Recommender Systems](https://mlsanity.com/paper/2411.13865/) (2024)
- [Tuning-Free LLM Can Build A Strong Recommender Under Sparse Connectivity And Knowledge Gap Via Extracting Intent](https://mlsanity.com/paper/2505.10900/) (2025)
- [GTR-Mamba: Geometry-to-Tangent Routing Mamba for Hyperbolic POI Recommendation](https://mlsanity.com/paper/2510.22942/) (2025)
- [Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation](https://mlsanity.com/paper/2606.03307/) (2026)
- [Reproducibility and Artifact Consistency of the SIGIR 2022 Recommender Systems Papers Based on Message Passing](https://mlsanity.com/paper/2503.07823/) (2025)

## Abstract (arXiv)

Knowledge graph (KG) plays an increasingly important role to improve the recommendation performance and interpretability. A recent technical trend is to design end-to-end models based on information propagation schemes. However, existing propagation-based methods fail to (1) model the underlying hierarchical structures and relations, and (2) capture the high-order collaborative signals of items for learning high-quality user and item representations. In this paper, we propose a new model, called Hierarchy-Aware Knowledge Gated Network (HAKG), to tackle the aforementioned problems. Technically, we model users and items (that are captured by a user-item graph), as well as entities and relations (that are captured in a KG) in hyperbolic space, and design a hyperbolic aggregation scheme to gather relational contexts over KG. Meanwhile, we introduce a novel angle constraint to preserve characteristics of items in the embedding space. Furthermore, we propose a dual item embeddings design to represent and propagate collaborative signals and knowledge associations separately, and leverage the gated aggregation to distill discriminative information for better capturing user behavior patterns. Experimental results on three benchmark datasets show that, HAKG achieves significant improvement over the state-of-the-art methods like CKAN, Hyper-Know, and KGIN. Further analyses on the learned hyperbolic embeddings confirm that HAKG offers meaningful insights into the hierarchies of data.