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    李挺, 金福生, 李荣华, 王国仁, 段焕中, 路彦雄. Light-HGNN:用于圈层内容推荐的轻量同质超图神经网络[J]. 计算机研究与发展. DOI: 10.7544/issn1000-1239.202220643
    引用本文: 李挺, 金福生, 李荣华, 王国仁, 段焕中, 路彦雄. Light-HGNN:用于圈层内容推荐的轻量同质超图神经网络[J]. 计算机研究与发展. DOI: 10.7544/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. 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. DOI: 10.7544/issn1000-1239.202220643

    Light-HGNN:用于圈层内容推荐的轻量同质超图神经网络

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

    • 摘要: 图神经网络和超图神经网络已经成为协同过滤推荐领域的研究热点. 然而实际场景中用户和项目的交互非常复杂,导致用户之间存在高阶的复杂关系,而普通图结构只能表达简单的成对关系,对网络结构的堆叠容易导致中间层表征的过度平滑,在稀疏场景下的用户建模、用户相似性发现与挖掘方面能力较弱;同时,异质超图神经网络的复杂结构使得模型的训练效率较低. 在以微信“搜一搜”等内容平台为代表的高度稀疏数据场景中,对于基于用户所属群体画像的圈层内容推荐任务,现有模型推荐效果差、用户表示的可解释性弱. 因此, 针对该类任务,提出了一个新的轻量同质超图神经网络模型,该模型包含用户交互数据至超图的转化、卷积生成用户表征序列、用户表征计算过滤. 模型首先将用户-项目交互数据转化为只含用户节点的同质超图并计算得到用户表征解耦序列初始值,随后根据超图拉普拉斯过滤矩阵进行信息传播与序列值的迭代生成,通过不使用激活层的卷积方法简化模型结构,并根据提出的均值差JK注意力机制为每个序列值生成权重矩阵. 最终,通过对解耦序列加权求和、过滤实现对用户表示的编码,并在真实数据集上进行实验验证了模型的相对更优效果.

       

      Abstract: Graph neural network and hypergraph neural network 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 model has 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 model.

       

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