IDEAS home Printed from https://ideas.repec.org/a/aza/jdmm00/y2023v12i2p116-126.html
   My bibliography  Save this article

Empowering content search: Leveraging the potential of machine learning

Author

Listed:
  • Jindal, Meenakshi

    (Netflix, Inc., USA)

  • Sekhri, Varun

    (Netflix, Inc., USA)

  • Low, Tiffany

    (Netflix, Inc., USA)

Abstract

In the digital age, the power of machine learning is harnessed to transform content search capabilities. This integration heralds a new era for content creation tools, granting editors and creators access to granular, frame-level content insights. These capabilities enable precision adjustments, enhancing the final product’s efficiency and quality. Recent years have witnessed remarkable shifts in using machine-generated data within media tools. However, harnessing the full potential of machine-learning techniques poses challenges due to the vast and diverse data generated by many algorithms. In response, Netflix has pioneered a groundbreaking solution: the Media Understanding Platform. This platform is a unifying abstraction layer across all Netflix studio applications, bridging the gap between client and machine-learning platforms. This paper illustrates the platform’s design and prowess through real-world examples of promotional media tools that enrich content discovery within Netflix’s expansive catalogue, offering a glimpse into the future of content search.

Suggested Citation

  • Jindal, Meenakshi & Sekhri, Varun & Low, Tiffany, 2023. "Empowering content search: Leveraging the potential of machine learning," Journal of Digital Media Management, Henry Stewart Publications, vol. 12(2), pages 116-126, December.
    • Handle: RePEc:aza:jdmm00:y:2023:v:12:i:2:p:116-126
    as

    Download full text from publisher

    File URL: https://hstalks.com/article/8229/download/
    Download Restriction: Requires a paid subscription for full access.
    File URL: https://hstalks.com/article/8229/
    Download Restriction: Requires a paid subscription for full access.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    digital assets search; machine learning; creative tools; standard schema; centralised platform;
    All these keywords.

    JEL classification:

    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

    Statistics

    Access and download statistics

    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:aza:jdmm00:y:2023:v:12:i:2:p:116-126. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Henry Stewart Talks (email available below). General contact details of provider: .

    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.