Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 12
3552-3556,3564

Study of listwise learning-to-rank recommendation method based on heterogeneous network analysis in scientific social network

Yue Fenga
Wang Hanrub
Zhang Xinyueb
Wang Gangb
a. School of Computer Science & Information Engineering, b. School of Management, Hefei University of Technology, Hefei 230009, China
DOI: 10.19734/j.issn.1001-3695.2019.09.0538

Abstract

In view of the fact that the existing recommendation methods for academic papers cannot make full use of the hete-rogeneous relations between entities in scientific social network, and most of them focus on the accuracy of predicted ratings, ignoring users' preference order, this paper proposed a heterogeneous network analysis based listwise learning-to-rank method. Firstly, it employed heterogeneous network analysis to fully explore the complex relations between entities in the scientific social network. On this basis, it integrated the information obtained from heterogeneous network analysis into the listwise method to optimize the ranking of the papers, which finally got the recommendation list of papers to researchers. This paper conducted experiments on the dataset of ScholarMate(one of the prevalent scientific social networks). And the experimental results show that the proposed method performs better than traditional recommendation methods, which illustrates the effectiveness of the proposed method.

Foundation Support

国家自然科学基金资助项目(71471054,91646111)
教育部人文社科基金资助项目(18YJC870025)
安徽省自然科学基金资助项目(1608085MG150)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.09.0538
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 12
Section: Algorithm Research & Explore
Pages: 3552-3556,3564
Serial Number: 1001-3695(2020)12-005-3552-05

Publish History

[2020-12-05] Printed Article

Cite This Article

岳峰, 王含茹, 张馨悦, 等. 科研社交网络中基于异质网络分析的列表级排序学习推荐方法研究 [J]. 计算机应用研究, 2020, 37 (12): 3552-3556,3564. (Yue Feng, Wang Hanru, Zhang Xinyue, et al. Study of listwise learning-to-rank recommendation method based on heterogeneous network analysis in scientific social network [J]. Application Research of Computers, 2020, 37 (12): 3552-3556,3564. )

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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