mlsanityData to 7 Oct 2026

Knowledge-Enhanced Top-K Recommendation in Poincar'e Ball

arXiv 2101.04852 ↗First posted 13 Jan 2021cs.IR cs.LGcited by 12

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Abstract (arXiv)

Personalized recommender systems are increasingly important as more content and services become available and users struggle to identify what might interest them. Thanks to the ability for providing rich information, knowledge graphs (KGs) are being incorporated to enhance the recommendation performance and interpretability. To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs. Furthermore, a hyperbolic attention network is employed to determine the relative importances of neighboring entities of a certain item. In addition, we propose an adaptive and fine-grained regularization mechanism to adaptively regularize items and their neighboring representations. Via a comparison using three real-world datasets with state-of-the-art methods, we show that the proposed model outperforms the best existing models by 2-16% in terms of NDCG@K on Top-K recommendation.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 25 of its 45 references that are in the map. First posted on arXiv 13 Jan 2021.

Builds on

25 of 45 references.

References · 25

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  2. 2018DARTS: Differentiable Architecture Searchcited by 1,782
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  7. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 342
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  9. 2018Representation Tradeoffs for Hyperbolic Embeddingscited by 204
  10. 2017Neural Factorization Machines for Sparse Predictive Analyticscited by 183
  11. 2017Variational Reasoning for Question Answering with Knowledge Graphcited by 170
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13 more
  1. 2018RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systemscited by 136
  2. 2017A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applicationscited by 126
  3. 2018Hyperbolic Attention Networkscited by 122
  4. 2019Knowledge Graph Convolutional Networks for Recommender Systemscited by 121
  5. 2019Hierarchical Gating Networks for Sequential Recommendationcited by 101
  6. 2019Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendationcited by 70
  7. 2018HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systemscited by 47
  8. 2020Reinforced Negative Sampling over Knowledge Graph for Recommendationcited by 40
  9. 2018Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendationcited by 39
  10. 2020Multi-Graph Convolution Collaborative Filteringcited by 27
  11. 2018Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influencecited by 11
  12. 2018Regularizing Matrix Factorization with User and Item Embeddings for Recommendationcited by 6
  13. 2018Gated Attentive-Autoencoder for Content-Aware Recommendationcited by 6

Built on by

Cited by year

Citing papers per year: 2021: 3; 2022: 3; 2023: 2; 2024: 3; 2025: 1.

12 papers.

  1. 2021Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendationcited by 12
  2. 2021Recommender systems based on graph embedding techniques: A comprehensive reviewcited by 8
  3. 2021HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendationcited by 4
  4. 2022Poincar'e Heterogeneous Graph Neural Networks for Sequential Recommendationcited by 2
  5. 2022Adapting Triplet Importance of Implicit Feedback for Personalized Recommendationcited by 2
  6. 2025Hyperbolic Contrastive Learning with Model-augmentation for Knowledge-aware Recommendationcited by 2
  7. 2023Lorentz Equivariant Model for Knowledge-Enhanced Hyperbolic Collaborative Filteringcited by 1
  8. 2024Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networkscited by 1
  9. 2024InBox: Recommendation with Knowledge Graph using Interest Box Embeddingcited by 1
  10. 2024Breaking Information Cocoons: A Hyperbolic Framework for Balancing Exploration and Exploitation in Recommender Systemscited by 1
  11. 2022Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendationcited by 0
  12. 2023When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-k Multi-Label Learningcited by 0