Xia Zhongxiu, Zhang Weiyu, Weng Ziqiang
School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250353, China.
Comput Intell Neurosci. 2021 Nov 5;2021:7716214. doi: 10.1155/2021/7716214. eCollection 2021.
In recent years, due to the rise of online social platforms, social networks have more and more influence on our daily life, and social recommendation system has become one of the important research directions of recommendation system research. Because the graph structure in social networks and graph neural networks has strong representation capabilities, the application of graph neural networks in social recommendation systems has become more and more extensive, and it has also shown good results. Although graph neural networks have been successfully applied in social recommendation systems, their performance may still be limited in practical applications. The main reason is that they can only take advantage of pairs of user relations but cannot capture the higher-order relations between users. We propose a model that applies the hypergraph attention network to the social recommendation system (HASRE) to solve this problem. Specifically, we take the hypergraph's ability to model high-order relations to capture high-order relations between users. However, because the influence of the users' friends is different, we use the graph attention mechanism to capture the users' attention to different friends and adaptively model selection information for the user. In order to verify the performance of the recommendation system, this paper carries out analysis experiments on three data sets related to the recommendation system. The experimental results show that HASRE outperforms the state-of-the-art method and can effectively improve the accuracy of recommendation.
近年来，由于在线社交平台的兴起，社交网络对我们的日常生活影响越来越大，社交推荐系统已成为推荐系统研究的重要研究方向之一。由于社交网络中的图结构和图神经网络具有强大的表示能力，图神经网络在社交推荐系统中的应用越来越广泛，并且也显示出了良好的效果。尽管图神经网络已成功应用于社交推荐系统，但其性能在实际应用中可能仍然受到限制。主要原因是它们只能利用用户对之间的关系，而无法捕捉用户之间的高阶关系。我们提出了一种将超图注意力网络应用于社交推荐系统的模型（HASRE）来解决这个问题。具体来说，我们利用超图对高阶关系进行建模的能力来捕捉用户之间的高阶关系。然而，由于用户朋友的影响不同，我们使用图注意力机制来捕捉用户对不同朋友的关注，并为用户自适应地建模选择信息。为了验证推荐系统的性能，本文对与推荐系统相关的三个数据集进行了分析实验。实验结果表明，HASRE优于现有方法，能够有效提高推荐的准确性。
