一种适用于业务流程定制环境的服务推荐方案

Translated title of the contribution: Service recommendation scheme suitable for business process customization environments

Yang Yu, Linfu Sun, Qishi Wu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In the personalized business process customization environment, recommendation technology is introduced to improve the user's customization efficiency, but the existing recommendation technology has many problems, such as the new users, the lack of diversity of recommendation results and low recommendation quality. To give full play to the advantages of recommended technologies and solve the above problems, a service recommendation scheme suitable for business process customization environment was proposed. In this scheme, the clustering algorithm was used to solve new user problems, then the collaborative filtering was optimized to improve the diversity of recommendation results. The content filtering was built to screen out "pseudo-neighbors" for improving the recommendation quality, and the candidate services were recommended to the user to improve customization efficiency. The experimental research proved that the scheme proposed could solve the problem of new users, improve the diversity of recommendation results and recommendation quality, and enhance the customization efficiency of the users.

Translated title of the contributionService recommendation scheme suitable for business process customization environments
Original languageChinese (Traditional)
Pages (from-to)692-702
Number of pages11
JournalJisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS
Volume25
Issue number3
DOIs
StatePublished - Mar 1 2019

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Science Applications
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Keywords

  • Business process customization
  • Cloud service platform
  • Collaborative filtering
  • Content-based filtering
  • Hybrid recommendation technology
  • User clustering

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