Recommended for you

  • Release version: Xanadu
  • Updated August 1, 2024
  • 2 minutes to read
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    Summarized using AI
    This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.

    Summary of Recommended for you

    TheRecommended for youwidget in ServiceNow displays catalog items and knowledge articles tailored to each employee, showing content that is most relevant based on their profile and usage patterns. It leverages machine learning (ML) algorithms using Employee Profile or HR Profile data to recommend popular and pertinent items. This personalized content appears on the Employee Center portal home page and the ServiceNow mobile app, providing users quick access to helpful resources.

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    Key Features

    • Personalized Content Recommendations: Uses ML to recommend catalog items and knowledge articles based on similar user profiles or popularity, depending on license entitlements.
    • Visual Presentation: Offers both detailed card views with titles, descriptions, and images, and simple list views showing titles only.
    • Content Preview: Provides previews of knowledge articles including title and description, and shows catalog item descriptions and pricing if available.
    • Configurable Display: Administrators can configure the widget to control the number of recommended items shown, widget title, and categories of items displayed.
    • Taxonomy Integration: Recommendations are aligned with portal taxonomy topics, requiring active taxonomy association and properly tagged content.

    Prerequisites and Requirements

    • Available on Employee Center version 23.0.7 and later.
    • ML-based recommendations require EC Pro or PI entitlements such as HRSD Pro, ITSM Pro, LSD, or WSD.
    • Employee Profile must be enabled to activate profile-based recommendations.
    • Active taxonomy must be associated with the Employee Center portal, and related content must have non-empty Taxonomy Topic fields.
    • Administrators should run "Set primary topics for Employee taxonomy" after installation.

    How it Works

    The widget highlights the most viewed knowledge articles and most requested catalog items over a configurable time period. Knowledge article recommendations are based on view counts, while catalog items are recommended according to request frequency. The recommendations respect the user’s language preferences, with an optional system property to control language fallback behavior.

    Configuration and Management

    • Portal administrators and developers can customize widget options to tailor the user experience.
    • Instance options allow control over widget title, number of displayed items, and selection of item categories.
    • Proper configuration ensures the widget effectively surfaces relevant content that enhances employee engagement and self-service efficiency.

    Recommended for you displays catalog items and knowledge articles, enabling you to view information that is most relevant to you.

    Content recommendations

    This widget highlights the most relevant catalog items and knowledge articles for the employee:
    • The Recommended for you widget can use an ML algorithm-based on Employee Profile or HR Profile to show popular content accessed by users having similar profile.
    • Recommended items are populated either based on ML or popularity of items based on your license entitlements. Underlying table for calculating similar user is determined by Employee Profile or HR profile.
      Note:
      When you install Employee Profile, the recommendations are populated based on the Employee Profile table.
    • The visual representation of items has a detailed card view with title, description, image, and a simple list view of only titles.
    • Employees can view related articles and catalog items on the portal home page and on ServiceNow mobile page​.
    • By default, the top five recommendations are visible on the widget. The widget provides users with a preview of a knowledge article with the title, description, and other useful information. Similarly, the catalog items show the description and the price of the product, if any.

    Prerequisites

    Learn about the version, license, and other requirements to use Recommended for you.
    • The widget is available for Employee Center versions 23.0.7 and later.
      Note:
      ML-based recommendations are available only for EC Pro customers or PI entitlement such as HRSD Pro, ITSM Pro, LSD, WSD.
    • Enable Employee Profile first to activate recommendations based on the Employee Profile table. Employee Profile is used to formulate recommendations that have similar profile users. For more information, see Manage the employee profile AI and ML model
    To make the widget visible on the Employee Center home page:
    1. Verify that an active taxonomy is associated to the Employee Center portal record​.
    2. Verify that you run Set primary topics for Employee taxonomy manually after installing the application.
    3. Verify you have articles or catalog item with non-empty Taxonomy Topic field​.
    For more information, see Configure the Recommended for you

    How Recommended for you works

    Administrators, developers, and advanced users can configure the widget options​. Recommended for you highlights the most viewed knowledge article and most requested catalog items across all employees for a time period defined in the Modify the Recommended for you widget display by the portal administrator.
    • Knowledge articles recommendations are based on the view count of article.
    • Catalog item recommendations are based on the number of times it is requested for.
    • The recommendations associated to the portal taxonomy are displayed on the home page.
    Note:
    The sys property (enable_topic_content_lang_fallback) disables the language fallback for KB articles, feature content in topics, and the Recommended for you widget. When the value is false, content isn't evaluated from the fallback language and only user preference language content is displayed.