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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
Citation: 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

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

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
  • 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.

  • [1]
    Zhou Yujia, Dou Zhicheng, Wei Bingzheng, et al. Group based personalized search by integrating search behaviour and friend network[C] //Proc of the 44th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2021: 92−101
    [2]
    Yu Junliang, Yin Hongzhi, Li Jundong, et al. Self-supervised multi-channel hypergraph convolutional network for social recommendation[C] //Proc of the Web Conf 2021. New York: ACM, 2021: 413−424
    [3]
    Wang Wen, Zhang Wei, Rao Jun, et al. Group-aware long- and short-term graph representation learning for sequential group recommendation[C] //Proc of the 43rd Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2020: 1449−1458
    [4]
    Gao Chen, Wang Xiang, He Xiangnan, et al. Graph neural networks for recommender system[C] //Proc of the 15th ACM Int Conf on Web Search and Data Mining. New York: ACM, 2022: 1623−1625
    [5]
    马帅,刘建伟,左信. 图神经网络综述[J]. 计算机研究与发展,2022,59(1):47−80

    Ma Shuai, Liu Jianwei, Zuo Xin. Survey on graph neural network[J]. Journal of Computer Research and Development, 2022, 59(1): 47−80 (in Chinese)
    [6]
    Xia Xin, Yin Hongzhi, Yu Junliang, et al. Self-supervised hypergraph convolutional networks for session-based recommendation[C] //Proc of the 45th AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2021: 4503−4511
    [7]
    Wu Jiancan, Wang Xiang, Feng Fuli, et al. Self-supervised graph learning for recommendation[C] //Proc of the 44th Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2021: 726−735
    [8]
    Ji Shuyi, Feng Yifan, Ji Rongrong, et al. Dual channel hypergraph collaborative filtering[C] //Proc of the 26th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining. New York: ACM, 2020: 2020−2029
    [9]
    Rendle S, Freudenthaler C, Gantner Z, et al. BPR: Bayesian personalized ranking from implicit feedback[J]. arXiv preprint, arXiv: 1205. 2618, 2012
    [10]
    Feng Yifan, You Haoxuan, Zhang Zizhao, et al. Hypergraph neural networks[C] //Proc of the 43rd AAAI Conf on Artificial Intelligence. Palo Alto, CA: AAAI, 2019: 3558−3565
    [11]
    Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[J]. arXiv preprint, arXiv: 1609. 02907, 2016
    [12]
    Zhang Ruochi, Zou Yuesong, Ma Jian. Hyper-SAGNN: A self-attention based graph neural network for hypergraphs[J]. arXiv preprint, arXiv: 1911. 02613, 2019
    [13]
    Bai Song, Zhang Feihu, Torr P H S. Hypergraph convolution and hypergraph attention[J]. arXiv preprint, arXiv: 1901. 08150, 2020
    [14]
    Hu Youpeng, Li Xunkai, Wang Yujie, et al. Adaptive hypergraph auto-encoder for relational data clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(3): 2231−2242
    [15]
    Teevan J, Liebling D J, Ravichandran G. Understanding and predicting personal navigation[C] //Proc of the 4th ACM Int Conf on Web Search and Data Mining. New York: ACM, 2011: 85−94
    [16]
    Dou Zhicheng, Song Ruihua, Wen Jirong. A large-scale evaluation and analysis of personalized search strategies[C]//Proc of the 16th Int Conf on World Wide Web. New York: ACM, 2007: 581−590
    [17]
    Carman M J, Crestani F, Harvey M, et al. Towards query log based personalization using topic models[C] //Proc of the 19th ACM Int Conf on Information and Knowledge Management. New York: ACM, 2010: 1849−1852
    [18]
    Wang Xiang, He Xiangnan, Wang Meng, et al. Neural graph collaborative filtering[C]//Proc of the 42nd Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2019: 165−174
    [19]
    He Xiangnan, Deng Kuan, Wang Xiang, et al. LightGCN: Simplifying and powering graph convolution network for recommendation[C] //Proc of the 43rd Int ACM SIGIR Conf on Research and Development in Information Retrieval. New York: ACM, 2020: 639−648
    [20]
    Ai Qingyao, Zhang Yongfeng, Bi Keping, et al. Explainable product search with a dynamic relation embedding model[J]. ACM Transactions on Information Systems, 2019, 38(1): 1−29
    [21]
    Liu Shang, Gu Wanli, Cong Gao, et al. Structural relationship representation learning with graph embedding for personalized product search[C] //Proc of the 29th ACM Int Conf on Information & Knowledge Management. New York: ACM, 2020: 915−924
    [22]
    Hu Binbin, Shi Chuan, Zhao W X, et al. Leveraging meta-path based context for top-n recommendation with a neural co-attention model[C] //Proc of the 24th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining. New York: ACM, 2018: 1531−1540
    [23]
    Ungar L H, Foster D P. Clustering methods for collaborative filtering[C]//Proc of the 15th AAAI Workshop on Recommendation Systems. Palo Alto, CA: AAAI, 1998: 114−129
    [24]
    Yang Xiwang, Guo Yang, Liu Yong. Bayesian-inference-based recommendation in online social networks[J]. IEEE Transactions on Parallel and Distributed Systems, 2012, 24(4): 642−651
    [25]
    Lee C H, Kim Y H, Rhee P K. Web personalization expert with combining collaborative filtering and association rule mining technique[J]. Expert Systems with Applications, 2001, 21(3): 131−137 doi: 10.1016/S0957-4174(01)00034-3
    [26]
    Paterek A. Improving regularized singular value decomposition for collaborative filtering[C]//Proc of the 13th KDD Cup and Workshop. New York: ACM, 2007: 5−8
    [27]
    Koren Y, Bell R, Volinsky C. Matrix factorization techniques for recommender systems[J]. Computer, 2009, 42(8): 30−37 doi: 10.1109/MC.2009.263
    [28]
    He Xiangnan, Liao Lizi, Zhang Hanwang, et al. Neural collaborative filtering[C]//Proc of the 26th Int Conf on World Wide Web. Geneva, Switzerland: IW3C2, 2017: 173−182
    [29]
    Jeh G, Widom J. SimRank: A measure of structural-context similarity[C]//Proc of the 8th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2002: 538−543
    [30]
    Gori M, Pucci A, Roma V, et al. ItemRank: A random-walk based scoring algorithm for recommender engines[C] //Proc of the 20th Int Joint Conf on Artificial Intelligence. San Francisco, CA: Margan Kaufmann, 2007: 2766−2771
    [31]
    He Xiangnan, Gao Ming, Kan M Y, et al. BiRank: Towards ranking on bipartite graphs[J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 29(1): 57−71
    [32]
    Berg R, Kipf T N, Welling M. Graph convolutional matrix completion[J]. arXiv preprint, arXiv: 1706. 02263, 2017
    [33]
    Ying R, He Ruining, Chen Kaifeng, et al. Graph convolutional neural networks for web-scale recommender systems[C] //Proc of the 24th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining. New York: ACM, 2018: 974−983
    [34]
    Hamilton W, Ying R, Leskovec J. Inductive representation learning on large graphs[C] //Proc of the 31st Int Conf on Neural Information Processing Systems. New York: Curran Associates, 2017: 1025−1035
    [35]
    Zhang Wentao, Yin Ziqi, Sheng Zeang, et al. Graph attention multi-layer perceptron[J]. arXiv preprint, arXiv: 2108. 10097, 2021
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