Learning Recommendations Cutover

You can now convert learning recommendations created by self-service users, administrators, and recommendation initiatives so that the recommendations are compatible with the recommendation enhancements announced in Release 24D and preserved when you enable those enhancements.  After running this tool, learning recommendations will no longer appear on the My Client Groups > Learning and Development > Learning Assignments and recommendation initiatives will no longer appear on the My Client Groups > Learning and Development > Learning Initiatives page.  Instead, they will all now appear on the new pages released in 24D accessed from My Client Groups > Learning and Development > Recommendations.

Recommendations generated automatically using any of the gaps, popular, or trending recommenders won't be converted. The 24D enhancements let you specify audiences for these recommenders, which wasn't possible before. And you need to set up new recommendation profiles for your recommender audiences.

This enhancement lets you take advantage of the latest features and enhancements for recommendations.

Steps to Enable

1. If you have extended the ORA_WLF_RECOMND_CATEGORY lookup with custom values, you need to add them to the new ORA_WLF_RECOM_CATEGORY lookup.  Use the Setup and Maintenance > Tasks panel tab > Search > Manage Common Lookups task. You can do this before or after the migration.
2. Enable the 24D recommendations features.
   • Item and Audience Selection Updates for Recommendations
   • Include HCM Journeys in Current Role Gap Recommendations
   • Audiences for Role Gaps and Popular Recommenders
3. Run the conversion tool. On the Tools > Scheduled Processes page, search for and submit the Learn Migration process using the Learn Recommendations Migration data correction option.
4. Re-ingest the fa-hcm-wlf-recommendation and fa-hcm-wlf-recommendationprofile search indexes.  On the Tools > Scheduled Processes page, search for and submit the ESS job to create index definition and perform initial ingest to OSCS process using the index name as input parameter

Tips And Considerations

Person Criteria and Learning Assignment Criteria learning audience selection types are no longer support in Redwood. They're replaced by Worker Assignment Filtered Lists and Learning Record Filtered Lists, respectively. Existing recommendations or recommendation initiatives that are converted will retain the original audience selection types, but they will be read-only and you can remove them from the resulting recommendation profile. We strongly recommend that you replace those original selection types with the equivalent filtered lists.

Previously, learning administrators could see only recommendation profiles for recommendation initiatives. They couldn't see any one-time recommendation profiles created by administrators. After completing the steps to enable, they'll now also see the recommendation profiles for these one-time recommendations.

Key Resources

For more information, see: 

• The Filtered Lists chapter of the Using Common Features for HCM guide
• Learning Recommendations Administration chapter of the Using Learning guide
• Recommending Trending Learning Items feature in release 24D What's New