基于注意力门控神经网络的社会化推荐算法  

Social Recommendation Algorithm Based on Attention Gated Neural Network

作　　者：邱叶 邵雄凯[1] 高榕 王春枝[1] 李晶[2] QIU Ye;SHAO Xiongkai;GAO Rong;WANG Chunzhi;LI Jing(School of Computer Science,Hubei University of Technology,Wuhan 430068,China;School of Computer Science,Wuhan University,Wuhan 430072,China)

机构地区：[1]湖北工业大学计算机学院,武汉430068 [2]武汉大学计算机学院,武汉430072

出　　处：《计算机工程与应用》2022年第5期112-118,共7页Computer Engineering and Applications

基　　金：国家自然科学基金(61170135);湖北工业大学博士启动基金(BSQD2019026)。

摘　　要：针对社会化推荐算法中存在的推荐准确率不高的问题,提出了一种多头注意力门控神经网络(MAGN)算法。具体来说,采用门控神经网络对输入的用户和用户-朋友对进行融合得到联合嵌入,利用注意力记忆网络来获取不同朋友在不同方面对用户的影响,利用多头注意力来获取在不同方面对用户影响程度偏高的几位朋友。采用门控神经网络将朋友影响和用户自身兴趣偏好进行混合,继而基于混合兴趣偏好对项目进行推荐。在两个公开真实数据集上进行实验进一步验证了所提方法的有效性。To address the problem of low recommendation accuracy in social recommendation algorithms,a multi-headed attention gating neural network(MAGN)algorithm is proposed.Specifically,a gated neural network is used to fuse the input user and the user’s friend pairs to obtain a joint embedding,then an attentional memory network is used to obtain the influence of different friends on the user in different aspects,and then multi-headed attention is used to obtain several friends who have a high degree of influence on the user in different aspects.Finally,a gated neural network is used to mix friend influence with the user’s own interest preferences,and items are then recommended based on mixed interest preferences.Experiments on two publicly real available datasets further validate the effectiveness of the proposed method.

关 键 词：推荐系统 深度学习 注意力机制 门控神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]