View in EDS PDF Full Text

Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-Directed Learning

Saved in:
Bibliographic Details
Title: Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-Directed Learning
Language: English
Authors: Zhiyong Qiu, Yingjin Cui (ORCID 0000-0002-5273-4300)
Source: SAGE Open. 2024 14(2).
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 16
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Tests/Questionnaires
Education Level: Higher Education
Postsecondary Education
Descriptors: College Students, Independent Study, Self Control, Library Materials, Selection Criteria, Selection Tools, Artificial Intelligence, Library Instruction, Library Services, Information Management, Access to Information, Database Management Systems, Information Retrieval, Probability
DOI: 10.1177/21582440241241981
ISSN: 2158-2440
Abstract: Faced the vast amount of information, choosing the appropriate materials is a prerequisite for effective self-directed learning. The recommendation algorithm is a kind of intelligent technology that can accurately locate the required information which the users care about most. However, many recommendation techniques experience can not be trained adequately in scenarios with small sample data and extremely sparse ratings. Moreover, DLRAs (Deep learning based Recommendation Algorithms) require high hardware support. The probabilistic graph (PG) can effectively represent the implicit complex relations among nodes, but it still has the problem of sparse data sensitivity. Therefore, we propose a Matrix-Factorization-based Probabilistic Graph Model for Recommendation Algorithm (MF-PGMRA): By matrix-factorizing the sparse rating matrix, the users and items are mapped to the user/item spaces, respectively; We employ the inner product to data-enhance and overcome the problems of sparse data and cold start; Then, we build Probabilistic Graph to construct the "user-item" latent spaces and estimate the probability distribution based on expectation maximization (EM), so as to predict the ratings; Finally, we built a library management system with the recommendation module to highlight the benefits of MF-PGMRA for students' subject learning. According to a questionnaire, we confirmed that the students are satisfied with the system from four aspects of speed, accuracy, usability and convenience, which can confirm that the library management system based on MF-PGMRA can efficiently and accurately recommend suitable materials for students from the huge amount of learning materials to improve students' self-directed learning efficiency.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1433439
Database: ERIC
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwF5tu1XvnbythgqNKfuuSkSAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDMJ4Xalk0AxAs58-gwIBEICBm14CK5cyn_f2Dqoix2oXvu8kGiac-4Vx60lmwtbbQhc_gTeJ_gp8j2uwS1VgbvJiCNpqOEyVcCrOGMjhRFY5zBWaz-V_JaWC3y7kHDrdZcrRZUp9Wb_b7rFOf4swRzOwzRRTgMH0boxAqJdho2VGWw7NrFrXleBbfL7raQ2n5_y0-US97kueBVyNU90d6HHO_-hgZbIjmoTW-lYe
Text:
  Availability: 0
Header DbId: eric
DbLabel: ERIC
An: EJ1433439
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-Directed Learning
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zhiyong+Qiu%22">Zhiyong Qiu</searchLink><br /><searchLink fieldCode="AR" term="%22Yingjin+Cui%22">Yingjin Cui</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-5273-4300">0000-0002-5273-4300</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22SAGE+Open%22"><i>SAGE Open</i></searchLink>. 2024 14(2).
– Name: Avail
  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 16
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research<br />Tests/Questionnaires
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Independent+Study%22">Independent Study</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Control%22">Self Control</searchLink><br /><searchLink fieldCode="DE" term="%22Library+Materials%22">Library Materials</searchLink><br /><searchLink fieldCode="DE" term="%22Selection+Criteria%22">Selection Criteria</searchLink><br /><searchLink fieldCode="DE" term="%22Selection+Tools%22">Selection Tools</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Library+Instruction%22">Library Instruction</searchLink><br /><searchLink fieldCode="DE" term="%22Library+Services%22">Library Services</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Management%22">Information Management</searchLink><br /><searchLink fieldCode="DE" term="%22Access+to+Information%22">Access to Information</searchLink><br /><searchLink fieldCode="DE" term="%22Database+Management+Systems%22">Database Management Systems</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Retrieval%22">Information Retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Probability%22">Probability</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/21582440241241981
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 2158-2440
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Faced the vast amount of information, choosing the appropriate materials is a prerequisite for effective self-directed learning. The recommendation algorithm is a kind of intelligent technology that can accurately locate the required information which the users care about most. However, many recommendation techniques experience can not be trained adequately in scenarios with small sample data and extremely sparse ratings. Moreover, DLRAs (Deep learning based Recommendation Algorithms) require high hardware support. The probabilistic graph (PG) can effectively represent the implicit complex relations among nodes, but it still has the problem of sparse data sensitivity. Therefore, we propose a Matrix-Factorization-based Probabilistic Graph Model for Recommendation Algorithm (MF-PGMRA): By matrix-factorizing the sparse rating matrix, the users and items are mapped to the user/item spaces, respectively; We employ the inner product to data-enhance and overcome the problems of sparse data and cold start; Then, we build Probabilistic Graph to construct the "user-item" latent spaces and estimate the probability distribution based on expectation maximization (EM), so as to predict the ratings; Finally, we built a library management system with the recommendation module to highlight the benefits of MF-PGMRA for students' subject learning. According to a questionnaire, we confirmed that the students are satisfied with the system from four aspects of speed, accuracy, usability and convenience, which can confirm that the library management system based on MF-PGMRA can efficiently and accurately recommend suitable materials for students from the huge amount of learning materials to improve students' self-directed learning efficiency.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1433439
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1433439
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/21582440241241981
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
    Subjects:
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Independent Study
        Type: general
      – SubjectFull: Self Control
        Type: general
      – SubjectFull: Library Materials
        Type: general
      – SubjectFull: Selection Criteria
        Type: general
      – SubjectFull: Selection Tools
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Library Instruction
        Type: general
      – SubjectFull: Library Services
        Type: general
      – SubjectFull: Information Management
        Type: general
      – SubjectFull: Access to Information
        Type: general
      – SubjectFull: Database Management Systems
        Type: general
      – SubjectFull: Information Retrieval
        Type: general
      – SubjectFull: Probability
        Type: general
    Titles:
      – TitleFull: Probabilistic Graph Model Based Recommendation Algorithm for Material Selection in Self-Directed Learning
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Zhiyong Qiu
      – PersonEntity:
          Name:
            NameFull: Yingjin Cui
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-electronic
              Value: 2158-2440
          Numbering:
            – Type: volume
              Value: 14
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: SAGE Open
              Type: main
ResultId 1