Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 1
34-39

Personalized tag recommendation based on potential tag mining and fine-grained preference

Li Hongmei1
Diao Xingchun1
Cao Jianjun2
Zhang Lei1
Feng Qin1
1. Army Engineering University, Nanjing 210007, China
2. the 63rd Research Institute, National University of Defense Technology, Nanjing 210007, China
DOI: 10.19734/j.issn.1001-3695.2018.05.0498

Abstract

To further improve the performance of personalized tag recommendation, this paper argued that traditional methods ignore the potential and informative tags hidden in the context of users and items. Aimed at this, this paper proposed a novel personalized tag recommendation method BPR-PITF-P based on potential tag mining and fine-grained preference. Firstly, BPR-PITF-P leveraged the context information of both users and got to mine potential and useful tags, and got three kinds of tags: positive tags, potential tags, and negative tags. Based on the above, it translated the traditional pairwise preference into fine-grained preference relationship among user-item post and tags. This kind of treatment helped alleviate the sparse problem of tagging data. Second, combined with pairwise interaction tensor factorization method to predict preference value, BPR-PITF-P modeled the preference relationship based on the optimization criteria of Bayesian personalized ranking, and developed a personalized tag recommendation model followed by optimization algorithm. The comparison results show that this proposed method could improve tag recommendation performance in the premise of guarantee convergence speed.

Foundation Support

国家自然科学基金资助项目
中国博士后科学基金资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.05.0498
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 1
Section: Algorithm Research & Explore
Pages: 34-39
Serial Number: 1001-3695(2020)01-007-0034-06

Publish History

[2020-01-05] Printed Article

Cite This Article

李红梅, 刁兴春, 曹建军, 等. 基于潜在标签挖掘和细粒度偏好的个性化标签推荐 [J]. 计算机应用研究, 2020, 37 (1): 34-39. (Li Hongmei, Diao Xingchun, Cao Jianjun, et al. Personalized tag recommendation based on potential tag mining and fine-grained preference [J]. Application Research of Computers, 2020, 37 (1): 34-39. )

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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