融合注意力机制的深度混合推荐算法  

Deep hybrid recommendation algorithm incorporating attention mechanism

作　　者：段超[1] 张婧[1] 何彬[1] 陈增照[2] Duan Chao;Zhang Jing;He Bin;Chen Zengzhao(Engineering Research Center for E-Learning,Faculty of Artificial Intelligence in Education,Central China Normal University,Wuhan 430079,China;National Engineering Laboratory for Educational Big Data,Faculty of Artificial Intelligence in Education,Central China Normal University,Wuhan 430079,China)

机构地区：[1]华中师范大学人工智能教育学部国家数字化学习工程技术研究中心,武汉430079 [2]华中师范大学人工智能教育学部教育大数据应用技术国家工程实验室,武汉430079

出　　处：《计算机应用研究》2021年第9期2624-2627,2634,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(62077022);国家教育部人文社科青年基金资助项目(20YJC880024)。

摘　　要：大量研究利用用户或项目的边信息来缓解视频推荐中的数据稀疏和冷启动问题,取得了一定的效果,但是没有关注辅助信息中的关键信息。针对此问题进行了研究,提出了一种融合双注意力机制的深度混合推荐模型。该模型通过融合自注意力机制的卷积神经网络挖掘项目端隐藏因子,同时融合自注意力机制的堆栈去噪自编码器提取用户端隐藏因子,深度挖掘项目端和用户端的重要信息。最后,通过结合概率矩阵分解实现视频评分预测。在两个公开数据集上的大量实验结果表明,提出的方法结果在已有ConvMF+、PHD、DUPIA等基线模型基础上有一定提升。User or item side information is widely used in numerous studies to alleviate the problems of data sparseness and cold start in video recommendation,which proved to be effective to some degree.However,the key part of auxiliary information was neglected in previous studies.This paper conducted research on this problem and proposed a deep hybrid recommendation model that incorporated dual attention mechanism.This model mined the hidden vectors on the item side through the convolutional neural network integrated with the self-attention mechanism and extracted the hidden vectors on the user side through the autoencoder fused with the self-attention mechanism.In this way,vital information on both the item side and user side was deeply mined,which was then combined with the classic probability matrix decomposition to realize score prediction.A large number of experimental results in two public datasets show that the results of the proposed model improves a certain extent compared with baseline models such as ConvMF+,PHD,and DUPIA.

关 键 词：双注意力机制 协同过滤 卷积神经网络 自编码器 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]