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华中师范大学学报(自然科学版)  2023, Vol. 57 Issue (5): 733-740    
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基于注意力机制的文本挖掘深度混合推荐模型
张 婧1, 陈增照2, 段 超3, 王 虎2
(1.江汉大学教育学院, 武汉 430065; 2.华中师范大学人工智能教育学部, 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)
Text mining deep hybrid recommendation model based on attention mechanism
ZHANG Jing1, CHEN Zengzhao2, DUAN Chao3, WANG Hu2
(1.School of Education, Jianghan University, Wuhan 430065,China;2.Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079,China;3.Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, Zhejiang,China)
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摘要 当前,从海量的互联网信息中获取满足用户需求的视频资源变得越来越困难,用户面临严重的信息焦虑和信息过载问题,然而各种辅助信息中蕴含着大量的与用户兴趣偏好及项目特征相关的信息并没有在经典推荐系统中得到利用.鉴于深度学习在特征提取和注意力机制在特征选择方面的突出表现,充分有效利用各种辅助信息缓解矛盾是当前研究的热点和难点问题.针对以上问题,该文提出了一种新颖的利用文本上下文信息的深度混合推荐方法.该方法将视频标题和视频简介组合,经过预训练的词嵌入模型Glove转化为词向量,通过融合多头注意力机制的卷积神经网络提取项目潜藏因子,再结合概率矩阵分解实现用户对视频资源的评分预测.在ML-100k、ML-1m、ML-10m、Amazon四个公开数据集上的实验结果表明,该研究提出的方法结果优于PMF、CDL、ConvMF等基线模型.
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张 婧
陈增照
段 超
王 虎
关键词 文本信息 多头注意力机制 卷积神经网络 协同过滤    
Abstract:At present, it is becoming more and more difficult to obtain video resources that meet the needs of users from massive internet information, and users are facing serious information anxiety and information overload problems. However, there is a large amount of information related to users' preferences and item characteristics, which has not been exploited in the classical recommendation systems. In view of the outstanding performance of deep learning in feature extraction and attention mechanism in feature selection, making full and effective use of various auxiliary information to alleviate contradictions is a hot and difficult issue in current research. In response to the above problems, a novel deep hybrid recommendation method using textual context information is proposed in this paper. This method combines the video title and video introduction, and converts pre-trained word embedding model Glove into word vectors. The convolution neural network combined with multi heads' attention mechanism is used to extract the hidden factors of items, and the probability matrix decomposition is used to predict the users' rating of video resources. The experimental results on four public datasets, ML-100k, ML-1m, ML-10m, and Amazon, show that the proposed method outperforms the baseline models such as PMF, CDL, and ConvMF.
Key wordscollaborative filtering    multi-headed attention    convolutional neural network    rating prediction
收稿日期: 2023-10-13     
引用本文:   
张 婧,陈增照,段 超,王 虎. 基于注意力机制的文本挖掘深度混合推荐模型[J]. 华中师范大学学报(自然科学版), 2023, 57(5): 733-740.
ZHANG Jing,CHEN Zengzhao,DUAN Chao,WANG Hu. Text mining deep hybrid recommendation model based on attention mechanism. journal1, 2023, 57(5): 733-740.
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https://journal.ccnu.edu.cn/zk/CN/     或     https://journal.ccnu.edu.cn/zk/CN/Y2023/V57/I5/733
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