[1]王 艳,彭 治,杜永萍.融合评分矩阵和评论文本的深度学习推荐模型[J].计算机技术与发展,2021,31(08):13-18.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 003]
　WANG Yan,PENG Zhi,DU Yong-ping.A Deep Learning Recommendation Model Combining Rating Matrix and Review Text[J].,2021,31(08):13-18.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 003]
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融合评分矩阵和评论文本的深度学习推荐模型()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年08期
页码:
13-18
栏目:
大数据分析与挖掘
出版日期:
2021-08-10

文章信息/Info

Title:
A Deep Learning Recommendation Model Combining Rating Matrix and Review Text
文章编号:
1673-629X(2021)08-0013-06
作者:
王 艳; 彭 治; 杜永萍
北京工业大学 信息学部,北京 100124
Author(s):
WANG Yan; PENG Zhi; DU Yong-ping
Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China
关键词:
推荐系统; 评论文本; 评分矩阵; 注意力机制; 深度学习
Keywords:
recommendation system; review text; rating matrix; attention mechanism; deep learning
分类号:
TP301. 6
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 08. 003
摘要:
用户对项目评分数据的稀疏性是影响推荐质量的主要因素之一,提出了融合评分数据和评论文本的深度学习模型,通过引入辅助信息缓解评分数据稀疏性的影响。 利用评论文本可以获取用户的偏好信息和项目特征,而评分数据中又包含了用户和项目之间的潜在关联。 现有的融合模型对评分数据的处理大多数都是采用矩阵分解方法,为了更好地利用评分数据中的有效信息,文中利用卷积神经网络处理评论文本,并引入注意力机制提取评论信息中具有代表性的评论,从而更好地表征用户偏好和项目特征。 利用深度神经网络处理评分数据提取其中的深度特征,将特征进行融合来预测出用户对项目的评分。 文中在 Amazon 数据集上进行验证,以均方误差 MSE 作为评价指标,结果表明所提出的模型优于多个优秀的基线模型。
Abstract:
The sparsity of user rating data is one of the main factors affecting recommendation quality. A deep learning model combining rating data and comment text is proposed to mitigate the impact of rating data sparsity by introducing auxiliary information. The review text can be used to obtain the user’s preference information and item characteristics,and the rating data contains the potential association between the user and the item. Most of the existing fusion models deal with rating data by using matrix factorization methods. In order to make better use of the effective information in the rating data,the convolutional neural network is used to process the comment text,and the attention mechanism is introduced to extract the representative comments from the comment information,so as to better characterize the user preferences and project characteristics. The deep neural network is used to process the score data to extract the deep features which are fused to predict the user’s score on the item. It is verified on the Amazon data set with the mean square error (MSE) as the evaluation index. The results show that the proposed model is better than many excellent baseline models.

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更新日期/Last Update: 2021-08-10