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

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.