IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v15y2023i10p323-d1250140.html
   My bibliography  Save this article

Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation Systems

Author

Listed:
  • Chin-Yi Chen

    (Department of Business Administration, Chung Yuan Christian University, Taoyuan 320, Taiwan)

  • Jih-Jeng Huang

    (Department of Computer Science & Information Management, Soochow University, Taipei City 100, Taiwan)

Abstract

Traditional movie recommendation systems are increasingly falling short in the contemporary landscape of abundant information and evolving user behaviors. This study introduced the temporal knowledge graph recommender system (TKGRS), a ground-breaking algorithm that addresses the limitations of existing models. TKGRS uniquely integrates graph convolutional networks (GCNs), matrix factorization, and temporal decay factors to offer a robust and dynamic recommendation mechanism. The algorithm’s architecture comprises an initial embedding layer for identifying the user and item, followed by a GCN layer for a nuanced understanding of the relationships and fully connected layers for prediction. A temporal decay factor is also used to give weightage to recent user–item interactions. Empirical validation using the MovieLens 100K, 1M, and Douban datasets showed that TKGRS outperformed the state-of-the-art models according to the evaluation metrics, i.e., RMSE and MAE. This innovative approach sets a new standard in movie recommendation systems and opens avenues for future research in advanced graph algorithms and machine learning techniques.

Suggested Citation

  • Chin-Yi Chen & Jih-Jeng Huang, 2023. "Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation Systems," Future Internet, MDPI, vol. 15(10), pages 1-13, September.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:10:p:323-:d:1250140
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/15/10/323/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/15/10/323/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chi-Yo Huang & Hong-Ling Hsieh & Hueiling Chen, 2020. "Evaluating the Investment Projects of Spinal Medical Device Firms Using the Real Option and DANP-mV Based MCDM Methods," IJERPH, MDPI, vol. 17(9), pages 1-41, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qüinny Soares Rocha & Rafaele Almeida Munis & Richardson Barbosa Gomes da Silva & Elí Wilfredo Zavaleta Aguilar & Danilo Simões, 2023. "Photovoltaic Solar Energy in Forest Nurseries: A Strategic Decision Based on Real Options Analysis," Sustainability, MDPI, vol. 15(5), pages 1-11, February.
    2. Salvador Garcia-Ayllon & Eloy Hontoria & Nolberto Munier, 2021. "The Contribution of MCDM to SUMP: The Case of Spanish Cities during 2006–2021," IJERPH, MDPI, vol. 19(1), pages 1-21, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:15:y:2023:i:10:p:323-:d:1250140. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.