Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 5
1311-1316

Ranking based hybrid deep tensor factorization model for group recommendation

Yang Li1
Wang Shihui1
Zhu Bo2
1. School of Computer Science & Information Engineering, Hubei University, Wuhan 430062, China
2. 709 Research Institute of China Shipbuilding Industry Corporation, Wuhan 420205, China
DOI: 10.19734/j.issn.1001-3695.2019.02.0067

Abstract

When modeling users' preferences, current researches of group recommendation usually ignored the mutual influence between group preference and individual preference and the problem of modeling initialization. To address these issues, this paper proposed a new group recommendation model called ranking based hybrid deep tensor factorization model, namely R-HDTF model. First of all, this paper developed a hybrid deep learning-based initialization method, which utilized deep denoising autoencoder to pretrain the initial values of the parameters for the R-HDTF model. Then, it proposed a pairwise tensor factorization model to capture the correlation among group, individual and item. Finally, it used the Bayesian personalized ranking(BPR) metric to optimize the loss objective function of tensor factorization and learn the parameters of the proposed model. Experimental results on real data sets show that the performance of the proposed algorithm outperforms traditional group recommendation algorithm.

Foundation Support

国家自然科学基金青年基金资助项目(11401187)
国家自然科学基金资助项目(61403132)
湖北省教育厅科学技术研究计划青年人才项目(Q20161001)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.02.0067
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 5
Section: Algorithm Research & Explore
Pages: 1311-1316
Serial Number: 1001-3695(2020)05-006-1311-06

Publish History

[2020-05-05] Printed Article

Cite This Article

杨丽, 王时绘, 朱博. 基于ranking的深度张量分解群组推荐算法 [J]. 计算机应用研究, 2020, 37 (5): 1311-1316. (Yang Li, Wang Shihui, Zhu Bo. Ranking based hybrid deep tensor factorization model for group recommendation [J]. Application Research of Computers, 2020, 37 (5): 1311-1316. )

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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