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MutualRec: Joint friend and item recommendations with mutualistic attentional graph neural networks
Journal of Network and Computer Applications ( IF 7.7 ) Pub Date : 2020-12-31 , DOI: 10.1016/j.jnca.2020.102954
Yang Xiao , Qingqi Pei , Tingting Xiao , Lina Yao , Huan Liu

Many social studies and practical cases suggest that people's consumption behaviors and social behaviors are not isolated but interrelated. However, most existing research either predicts users' consumption preference or recommends friends to users without dealing with them simultaneously. In this paper, we propose a novel framework called MutualRec that jointly accomplishes the two tasks based on graph neural networks, attention mechanisms, and mutualistic model. MutualRec first uses a spatial attention layer and a spectral attention layer to learn latent embeddings from observed data, and then merges them via a mutualistic attention layer. The first two layers can relieve data sparsity without violating users' preference sequence, while the last captures the relationship between user’ consumption and social behaviors. We demonstrate the effectiveness of MutualRec in both social recommendation and link prediction via extensive experiments.



中文翻译:

MutualRec:带有交互注意图神经网络的联合好友和物品推荐

许多社会研究和实际案例表明,人们的消费行为和社会行为不是孤立的而是相互联系的。但是,大多数现有研究要么预测用户的消费偏好,要么向用户推荐朋友,而不同时与他们打交道。在本文中,我们提出了一个称为MutualRec的新颖框架,该框架基于图神经网络,注意力机制和互惠模型共同完成两项任务。MutualRec首先使用空间关注层和光谱关注层从观察到的数据中学习潜在的嵌入,然后通过相互关注的层将它们合并。前两层可以缓解数据稀疏性,而不会违反用户的偏好顺序,而最后一层则捕获用户的消费与社交行为之间的关系。

更新日期:2021-01-10
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