Light-HGNN: Lightweight Homogeneous Hypergraph Neural Network for Circle Content Recommendation

Li Ting; Jin Fusheng; Li Ronghua; Wang Guoren; Duan Huanzhong; Lu Yanxiong

doi:10.7544/issn1000-1239.202220643

Journal of Computer Research and Development > 2024 > 61(4): 877-888. > DOI: 10.7544/issn1000-1239.202220643 CSTR: 32373.14.issn1000-1239.202220643

Li Ting, Jin Fusheng, Li Ronghua, Wang Guoren, Duan Huanzhong, Lu Yanxiong. Light-HGNN: Lightweight Homogeneous Hypergraph Neural Network for Circle Content Recommendation[J]. Journal of Computer Research and Development, 2024, 61(4): 877-888. DOI: 10.7544/issn1000-1239.202220643

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Light-HGNN: Lightweight Homogeneous Hypergraph Neural Network for Circle Content Recommendation

1.
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081
2.
Tencent Technology (Beijing) Co., Ltd., Beijing 100080

Funds: This work was supported by the National Key Research and Development Program of China（2020AAA0108503）and the National Natural Science Foundation of China ( 62272045).

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Author Bio:
Li Ting: born in 1996. Master candidate. Student member of CCF. His main research interest includes graph neural network

Jin Fusheng: born in 1977. PhD, associate professor, master supervisor. His main research interests include software architecture, machine learning, and deep learning

Li Ronghua: born in 1985. PhD, professor, PhD supervisor. His main research interests include graph data management and mining, graph computing system, and graph neural network

Wang Guoren: born in 1966. PhD, professor, PhD supervisor. His main research interests include uncertain data management and data intensive computing

Duan Huanzhong: born in 1985. Master. His main research interests include machine learning and data mining

Lu Yanxiong: born in 1984. Master. His main research interests include natural language processing and machine learning
Received Date: July 23, 2022
Revised Date: May 18, 2022
Available Online: November 13, 2023

Graphical Abstract

Abstract

Abstract

Graph neural network and hypergraph neural network (HGNN) have become research hotspots in the field of collaborative filtering recommendation. However, the interaction between users and projects in actual scenarios is very complex. As a result, there are high-order complex relationships among users, while ordinary graph structures can only express simple paired relationships. Stacking network structures easily leads to excessive smoothness of middle-tier representations, and the ability of user modeling, user similarity discovery and mining in sparse scenarios is weak. At the same time, the complex structure of heterogeneous hypergraph neural network makes the training efficiency of the model low. In the highly sparse data scene represented by WeChat “Search and Search” and other content platforms, the existing models have poor recommendation effect and weak interpretability of user representation for the circle content recommendation task based on the portrait of the user’s group. Therefore, for this kind of task, a new lightweight homogeneous hypergraph neural network model is proposed, which includes three parts: the transformation of user interaction data into hypergraph, the generation of user representation sequence by convolution, and the calculation and filtering of user representation. Firstly, the user-item interaction data is transformed into a homogeneous hypergraph with only user nodes, and the initial value of user representation decoupling sequence is calculated. Then information propagation and sequence values are iteratively generated according to the Laplacian filter matrix of hypergraph, the model structure is simplified by convolution method without activation layer, and the weight matrix is generated for each sequence value according to the proposed mean difference JK attention mechanism. Finally, the coding of user representation is realized by weighted summation and filtering of decoupled sequences, and experiments on real data sets verify the relatively better effect of the proposed model.

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References (35)

References

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