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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 4
1058-1061,1068

Recommendation model of deep neural network combining rating matrix and review text

Zhou Chuanhua1a,1b,2
Yu Cai1a
Lu Yong1a
1. a. School of Management Science & Engineering, b. Key Laboratory of Multidisciplinary Management & Control of Complex Systems of Anhui Higher Education Institutes, Anhui University of Technology, Ma'anshan Anhui 243002, China
2. School of Computer Science & Technology, University of Science & Technology of China, Hefei 230026, China
DOI: 10.19734/j.issn.1001-3695.2020.04.0089

Abstract

In view of the problems of sparse user rating matrix data set in personalized recommendation and insufficient utilization of user and item description information, this paper proposed a DeepRec that combined the rating matrix and comment text. Firstly, it obtained a text collection of user preference features and item attribute features through data preprocessing and then input the text collections into the convolutional neural network for training to obtain deep nonlinear features of users and items. At the same time, it input rating matrix into the multi-layer perceptron to get the user implicit representation, and integrated the user implicit representations. Secondly, it used multi-layer perceptron to model users and items implicit representations to personalize recommendations for users. Finally, this paper conducted a comparative experiment based on three datasets with RMSE as the evaluation indexes. The results show that the prediction error of DeepRec is lower, which effectively improves the recommendation accuracy.

Foundation Support

国家自然科学基金资助项目(71772002,61702006)
复杂系统多学科管理与控制安徽普通高校重点实验室资助项目(CS2020-04)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.04.0089
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 4
Section: Algorithm Research & Explore
Pages: 1058-1061,1068
Serial Number: 1001-3695(2021)04-017-1058-04

Publish History

[2021-04-05] Printed Article

Cite This Article

周传华, 于猜, 鲁勇. 融合评分矩阵和评论文本的深度神经网络推荐模型 [J]. 计算机应用研究, 2021, 38 (4): 1058-1061,1068. (Zhou Chuanhua, Yu Cai, Lu Yong. Recommendation model of deep neural network combining rating matrix and review text [J]. Application Research of Computers, 2021, 38 (4): 1058-1061,1068. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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