research-article

HGCH: A Hyperbolic Graph Convolution Network Model for Heterogeneous Collaborative Graph Recommendation

Authors:

Ning WuAuthors Info & Claims

CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management

Pages 3186 - 3196

https://doi.org/10.1145/3627673.3679701

Published: 21 October 2024 Publication History

Abstract

User-item interaction data in collaborative filtering and graph modeling tasks often exhibit power-law characteristics, which suggest the suitability of hyperbolic space modeling. Hyperbolic Graph Convolution Neural Networks (HGCNs) are a novel technique that leverages the advantages of GCN and hyperbolic space, and then achieves remarkable results. However, existing HGCN methods have several drawbacks: they fail to fully leverage hyperbolic space properties due to arbitrary embedding initialization and imprecise tangent space aggregation; they overlook auxiliary information that could enrich the collaborative graph; and their training convergence is slow due to margin ranking loss and random negative sampling. To overcome these challenges, we propose Hyperbolic Graph Collaborative for Heterogeneous Recommendation (HGCH), an enhanced HGCN-based model for collaborative filtering that integrates diverse side information into a heterogeneous collaborative graph and improves training convergence speed. HGCH first preserves the long-tailed nature of the graph by initializing node embeddings with power law prior; then it aggregates neighbors in hyperbolic space using the gyromidpoint method for accurate computation; finally, it fuses multiple embeddings from different hyperbolic spaces by the gate fusion with prior. Moreover, HGCH employs a hyperbolic user-specific negative sampling to speed up convergence. We evaluate HGCH on four real datasets, and the results show that HGCH achieves competitive results and outperforms leading baselines, including HGCNs. Extensive ablation studies further confirm its effectiveness.

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Cited By

Tu LMeng SLiu XLiu GBeheshti AZhang X(2024)Semantic Similarity-Based Graph Contrastive Learning for Recommender SystemWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0570-5_2(17-31)Online publication date: 30-Nov-2024
https://doi.org/10.1007/978-981-96-0570-5_2

Index Terms

HGCH: A Hyperbolic Graph Convolution Network Model for Heterogeneous Collaborative Graph Recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Conferences

CIKM '24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management

October 2024

5705 pages

ISBN:9798400704369

DOI:10.1145/3627673

General Chairs:
Edoardo Serra
Boise State University, USA
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Francesca Spezzano
Boise State University, USA

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Published: 21 October 2024

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Cited By

Tu LMeng SLiu XLiu GBeheshti AZhang X(2024)Semantic Similarity-Based Graph Contrastive Learning for Recommender SystemWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0570-5_2(17-31)Online publication date: 30-Nov-2024
https://doi.org/10.1007/978-981-96-0570-5_2

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