【摘要】 典型的社交推荐方法都受限于对用户的行为建模，如用户与用户之间的社交行为、用户与物品之间的交互行为，而忽略了用户感兴趣的多个物品之间存在的潜在相关性，导致信息丢失。在数据稀疏的推荐场景中，用户行为的稀疏性导致系统可用的信息不足，因此需要引入具有丰富内涵的物品关系作为辅助信息。文中致力于融合用户行为和辅助信息共同建模用户的偏好，以提升推荐的准确性。推荐系统中的数据大多可以表示为图结构，例如用户的社交行为、交互行为和物品关系，可以转化为用户-用户图、用户-物品图和物品-物品图。图神经网络在处理大规模图形数据方面颇有成效，建立一个包含物品关系的图神经网络推荐框架面临巨大的挑战：1）物品-物品关系是隐式的；2）用户-用户图、用户-物品图、物品-物品图，是3种不同类型的图；3）用户与用户、用户与物品、物品与物品之间的联系都具有异质性。为了解决上述问题，文中提出了一种新的基于图神经网络的社交推荐方法（PEVGraphRec），引入了一种数学上的方法显式地构建物品间的连接，该模型能够内在地结合3种不同的图，以便更好地学习用户偏好。最后，提出了注意力机制来综合地考虑不同信息的权重。在3个真实数据集上进行实验，实验结果证明了所提方法的有效性。更多还原

【Abstract】 Typical social recommendation methods are limited by modeling user behavior,such as social behavior between users,interaction behavior between users and items.However,the potential correlation between multiple items that users are interested in is ignored,leading to information loss.In recommendation scenarios with sparse data,the sparsity of user behavior leads to insufficient information available in the system,so it is necessary to introduce item relationships with rich connotations as auxiliary information.This papaer aims to integrate user behavior and auxiliary information to jointly model user preferences,so as to improve the accuracy of recommendations.Most of the data in the recommendation system can be expressed as a graph structure,such as user’s social behavior,user’s interactive behavior and item relationship,which can be converted into user-user graph,user-item graph and item-item graph.Graph neural networks(GNN)are effective in processing large-scale graphic data,and building a framework with item relations based-GNN for social recommendations is facing considerable challenges:1)the itemitem relationship is implicit;2)user-user graph,user-item graph,and item-item graph are three different types of graphs;3)the relationship between user and user,user and item,item and item is heterogeneous.In order to solve the above problems,this paper proposes a new social recommendation method based on graph neural network,PEVGraphRec,which introduces a mathematical way to explicitly construct connections between items.Thismodel inherently combines three different graphs to better learn user preference.Finally,an attention mechanism is proposed to consider the weight of different information comprehensively.Comprehensive experiments on three real-world datasets verify the effectiveness of the proposed framework.更多还原