View in EDS HTML Full Text PDF Full Text

Intelligent Optimization Method of Resource Recommendation Service of Mobile Library Based on Digital Twin Technology.

Saved in:
Bibliographic Details
Title: Intelligent Optimization Method of Resource Recommendation Service of Mobile Library Based on Digital Twin Technology.
Authors: Shang, Shanshan1 (AUTHOR), Yu, Zikai2 (AUTHOR), Geng, Aili1 (AUTHOR), Xu, Xiuxiu1 (AUTHOR), Ma, Huizhen1 (AUTHOR), Wang, Guozhong3 (AUTHOR)
Source: Computational Intelligence & Neuroscience. 8/27/2022, p1-10. 10p.
Subjects: Mobile libraries, Digital twin, Library cooperation, Digital technology, Information retrieval, Intelligent transportation systems
Abstract: In order to improve the Resource Recommendation and sharing ability of mobile library, an intelligent optimization model of Mobile Library Resource Recommendation Service Based on digital twin technology is proposed. Build the association rule feature distribution set of mobile library resource recommendation service, carry out text information retrieval in the process of Mobile Library Resource Recommendation and sharing, carry out semantic correlation feature registration according to the retrieval preference of mobile library reading user object, establish the association rule data set of mobile library reading user object preference for mobile library Resource Recommendation and sharing, carry out feature block processing, and analyze the library reader preference. Complete the collaborative filtering recommendation of Mobile Library Resource Recommendation sharing. The simulation results show that the collaborative recommendation under the intelligent optimization mode of mobile library resource recommendation service using this method has high accuracy and good confidence level, which improves the intelligent level of Mobile Library Resource Recommendation and user satisfaction. [ABSTRACT FROM AUTHOR]
Copyright of Computational Intelligence & Neuroscience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 158754646
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Intelligent Optimization Method of Resource Recommendation Service of Mobile Library Based on Digital Twin Technology.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Shang%2C+Shanshan%22">Shang, Shanshan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yu%2C+Zikai%22">Yu, Zikai</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Geng%2C+Aili%22">Geng, Aili</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Xiuxiu%22">Xu, Xiuxiu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Huizhen%22">Ma, Huizhen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Guozhong%22">Wang, Guozhong</searchLink><relatesTo>3</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Computational+Intelligence+%26+Neuroscience%22">Computational Intelligence & Neuroscience</searchLink>. 8/27/2022, p1-10. 10p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Mobile+libraries%22">Mobile libraries</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Library+cooperation%22">Library cooperation</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+technology%22">Digital technology</searchLink><br /><searchLink fieldCode="DE" term="%22Information+retrieval%22">Information retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+transportation+systems%22">Intelligent transportation systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In order to improve the Resource Recommendation and sharing ability of mobile library, an intelligent optimization model of Mobile Library Resource Recommendation Service Based on digital twin technology is proposed. Build the association rule feature distribution set of mobile library resource recommendation service, carry out text information retrieval in the process of Mobile Library Resource Recommendation and sharing, carry out semantic correlation feature registration according to the retrieval preference of mobile library reading user object, establish the association rule data set of mobile library reading user object preference for mobile library Resource Recommendation and sharing, carry out feature block processing, and analyze the library reader preference. Complete the collaborative filtering recommendation of Mobile Library Resource Recommendation sharing. The simulation results show that the collaborative recommendation under the intelligent optimization mode of mobile library resource recommendation service using this method has high accuracy and good confidence level, which improves the intelligent level of Mobile Library Resource Recommendation and user satisfaction. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Computational Intelligence & Neuroscience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=158754646
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1155/2022/3582719
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 10
        StartPage: 1
    Subjects:
      – SubjectFull: Mobile libraries
        Type: general
      – SubjectFull: Digital twin
        Type: general
      – SubjectFull: Library cooperation
        Type: general
      – SubjectFull: Digital technology
        Type: general
      – SubjectFull: Information retrieval
        Type: general
      – SubjectFull: Intelligent transportation systems
        Type: general
    Titles:
      – TitleFull: Intelligent Optimization Method of Resource Recommendation Service of Mobile Library Based on Digital Twin Technology.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Shang, Shanshan
      – PersonEntity:
          Name:
            NameFull: Yu, Zikai
      – PersonEntity:
          Name:
            NameFull: Geng, Aili
      – PersonEntity:
          Name:
            NameFull: Xu, Xiuxiu
      – PersonEntity:
          Name:
            NameFull: Ma, Huizhen
      – PersonEntity:
          Name:
            NameFull: Wang, Guozhong
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 27
              M: 08
              Text: 8/27/2022
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 16875265
          Titles:
            – TitleFull: Computational Intelligence & Neuroscience
              Type: main
ResultId 1