基于注意力循环神经网络的联合深度推荐模型  

Joint deep recommendation model based on attention recurrent neural network

作　　者：郭东坡 何彬[2] 张明焱 段超 GUO Dongpo;HE Bin;ZHANG Mingyan;DUAN Chao(Jianghan University,Wuhan 430056,China;Central China Normal University,Wuhan 430079,China;Zhejiang Provincial Key Laboratory of Intelligent Education Technology and Application,Zhejiang Normal University,Jinhua 321004,China)

机构地区：[1]江汉大学,湖北武汉430056 [2]华中师范大学,湖北武汉430079 [3]浙江师范大学浙江省智能教育技术与应用重点实验室,浙江金华321004

出　　处：《现代电子技术》2025年第1期80-84,共5页Modern Electronics Technique

基　　金：2021年国家自然科学基金:面向智能化作业辅导的图文混合题可理解机器解答研究(62177024);2023年国家自然科学基金:面向知识整合的碎片化学习及学习路径引导研究(62207027);2022年度湖北省教育科学规划课题:基于智慧学习环境的学习者模型构建及个性化学习路径推荐研究(2022GB217);湖北省教育厅科学技术研究计划指导性项目:面向数学课程的自适应学习路径推荐研究(B2022560)。

摘　　要：为了向用户推荐符合兴趣偏好的项目,设计一种基于注意力循环神经网络的联合深度推荐模型。将双层注意力机制设置于网络中,该模型由五个部分构成,在输入层中生成联合深度推荐模型的输入矩阵,通过序列编码层对项目评论文本语义展开正向和反向编码,获得隐藏状态输出,并将其输入双层注意力机制中,提取项目特征,利用全连接层提取用户偏好特征。在预测层中建立项目与用户的交互模型,获得项目评分,为用户推荐高评分的项目。为了提高模型精度,加权融合MSE损失函数、CE损失函数和RK损失函数建立组合损失函数,对深度联合训练模型展开训练,提高模型的推荐性能。仿真结果表明,所提方法具有良好的推荐效果,能够适应不断变化的市场需求和用户行为。A joint deep recommendation model based on attention recurrent neural network is designed to recommend projects that meet user interests and preferences.The attention mechanism with double layers is set in the network.The designed model consists of five parts.The input matrix of the joint deep recommendation model is generated in the input layer.By the sequence coding layer,the semantics of the project comment text is encoded forward and backward to obtain the hidden state output.The hidden state output is input into the attention mechanism with double layers to extract the project features.The fully-connected layer is used to extract user preference features.An interaction model between projects and users is established in the prediction layer,so as to obtain project ratings and recommend high-rated projects for users.In order to improve the accuracy of the model,the combined loss function is established based on the weighted integration of MSE loss function,CE loss function and RK loss function.The deep joint training model is trained to improve the recommendation performance of the model.The simulation results show that the proposed method has good recommendation effect,so it can adapt to the changing market demand and user behavior.

关 键 词：双层注意力机制 循环神经网络 用户偏好 组合损失函数 交互模型 联合深度推荐模型 

分 类 号：TN711-34[电子电信—电路与系统] TP183[自动化与计算机技术—控制理论与控制工程]