Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 8
2400-2403

Resource allocation in statistical recommendation model

Cheng Yingchao1,2
Hao Zhifeng1,3
Cai Ruichu1
1. School of Computer Science & Technology, Guangdong University of Technology, Guangzhou 510006, China
2. Dept. of Statistics, Texas A&M University, Texas College Station 77840, USA
3. School of Mathematics & Big Data, Foshan University, Foshan Guangdong 510000, China
DOI: 10.19734/j.issn.1001-3695.2019.02.0061

Abstract

Statistical recommendation models in information systems require the acquisition, analysis, and aggregation of data from multiple sources. These heterogeneous multi-source data may have significant differences in features and values, thus affecting model performance. In order to improve the overall performance of the statistical recommendation model, this research used convex optimization theory and method to solve the resource allocation problem of heterogeneous data sources in the statistical recommendation model. This work compared the performance changes of a recommendation model under different data-source-resource-configurations. The experimental results show that the proposed resource allocation algorithm effectively improves the model performance in the two main evaluation indexes of NDCG(normalized discounted cumulative gain) and recall rate. The conclusion is that, for multiple heterogeneous data sources, proper resource partitioning and allocation strategies can significantly affect the overall performance of the recommendation model.

Foundation Support

国家自然科学基金资助项目(61472089,61572143)
NSFC-广东联合基金资助项目(U1501254)
广东省自然科学基金资助项目(2014A030306004,2014A030308008)
广东省科技计划资助项目(2013B051000076,2015B010108006,2015B010131015)
广东特支计划资助项目(2015TQ01X140)
广州市珠江科技新星项目(201610010101)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.02.0061
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 8
Section: Algorithm Research & Explore
Pages: 2400-2403
Serial Number: 1001-3695(2020)08-033-2400-04

Publish History

[2020-08-05] Printed Article

Cite This Article

成英超, 郝志峰, 蔡瑞初. 统计推荐模型中的异构数据源资源配置 [J]. 计算机应用研究, 2020, 37 (8): 2400-2403. (Cheng Yingchao, Hao Zhifeng, Cai Ruichu. Resource allocation in statistical recommendation model [J]. Application Research of Computers, 2020, 37 (8): 2400-2403. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)