协同过滤算法被广泛运用于各类大数据的推荐系统中, 能够向用户推荐与该用户类似的用户感兴趣的信息。 随着深度学习, 尤其是图神经网络的发展, 基于图神经网络的协同过滤算法受到了越来越多的关注。 基于图结构的协同过滤模型通常将用户与条目的交互信息建模为二部图, 然后利用二部图的高阶连通性建模捕获用户与条目之间的隐藏关系。 但是, 这种二部图模型没有将用户之间的相似关系和条目之间的相似关系明确建模。 此外, 二部图的稀疏性会产生图中高阶连通性依赖问题。 为此, 该文提出了一种基于异质图卷积神经网络的协同过滤模型, 将用户之间的相似度和条目之间的相似度显式地编码到图结构中, 使得用户与条目的交互关系被建模成异质图。 异质图结构使用户之间的相似度与条目之间的相似度能被直接捕获, 降低了对高阶连通性的依赖, 同时缓解了二部图过于稀疏的问题。 该文在4个典型的数据集上进行了实验, 并与4种经典模型进行了对比, 结果表明所提出的模型效果较好。
Abstract
[Objective] Collaborative filtering algorithms are widely used in various recommendation systems and can be used to recommend information of interest to users similar to the user based on historical data. Recently, collaborative filtering algorithms based on graph neural networks have become one of the hot research topics. A collaborative filtering model based on a graph structure usually encodes the interaction between users and information items as a two-part diagram, and high-order connectivity modeling of the bipartite graph can be used to capture the hidden relationship between the user and the item. However, this bipartite graph model does not explicitly obtain the similarity relationship between users and between items. Additionally, the bipartite graph sparsity causes high-order connectivity dependence problems in the model. [Methods] Herein, a collaborative filtering model is proposed based on a heterogeneous graph convolutional neural network that explicitly encodes the similarities between users and that between items into the graph structure so that the interaction relationships between users and between items are modeled as a heterogeneous graph. The heterogeneous graph structure allows the similarities between users and between items to be directly captured, reducing the need for high-order connectivity and alleviating the bipartite graph sparsity problem. [Results] We conducted experiments on four typical datasets and compared the results using four typical methods. The results showed that our model achieved better experimental results than the traditional collaborative filtering models and existing graph neural network models. Moreover, based on the different types of edges, different similarity methods, and different similarity thresholds, our model obtained better experimental results. [Conclusions] Our model explicitly encodes the similarities between users and between items into the heterogeneous graph structure as edges so that the model can directly learn these similarities during training to get the embedded information of users and items. The proposed model alleviates the sparsity and high-order connectivity modeling problems of bipartite graphs. The collaborative filtering model based on heterogeneous graph neural networks can also fully capture the interaction relationships between users and items through low-order connectivity in the graph.
关键词
图卷积神经网络 /
协同过滤 /
推荐系统 /
二部图 /
异质图
Key words
graph convolutional neural network /
collaborative filtering /
recommendation system /
bipartite graph /
heterogeneous graph
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基金
中央高校基本科研业务费项目(BLX202003)
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