SuccessFactors Personalized Learning Recommendations
A growing body of research advises that overall employee success is likely to increase when employees are able to learn at their own pace with a variety of learning types available to them. Personalizing learner’s courses enables them to access a unique learning experience based upon their individual profile, rather than receiving instruction through a standard, paced curriculum.
Based on the recent trends noticed in sectors like large streaming services such as Netflix, classic e-commerce providers such as Amazon, Customers expect similar functionality in finding courses within learning management.
SAP SuccessFactors Learning has developed the personalized learning recommendation platform to fulfill the business need. With Personalized learning recommendations, SAP SuccessFactors Learning enables users to find relevant courses from their learning libraries without the need to search through the library.
Personalized Learning Recommendations are powered by machine learning capabilities with SAP Leonardo. By applying machine learning algorithms learners get these recommendations based on the information about a learner, including their profile information and learning activity. Recommendations engine deliver best-fit recommendations by identifying topics of interest for every learner, hence displaying matched preferences. A user can be recommended any course that is accessible in the library, including Open Content Network courses. Those courses will not be recommended to the user which are already on the user’s learning plan, or have been bookmarked by users, or have already been completed by the user.
Personalized Learning Recommendations Configuration
Prerequisites to enable Personalized Learning Recommendations
- Your system must be integrated with the SuccessFactors platform.
- Personalized Learning Recommendations is available to customers who are part of the Early Adopter Program. Interested customers can request to join the SAP Early Adopter Care
- The Recommendations Engine presently supports only English locales. You must have English language content because users with a preferred locale other than English will not be able to view Personalized Recommendations.
Supported Locales for Personalized Learning Recommendations
For Personalized Learning Recommendations only the following English locales are supported:
- English Canada
- English United Kingdom
How Personalized Recommendations are generated
Personalized Recommendations are generated by applying machine learning to use data that includes user attributes and data related to user activity. This data is synchronized periodically from Learning to the recommendations engine on SAP Leonardo.
There are two SAP Leonardo data centers – one in Europe and the other in the US. The data from your Learning tenant will be transferred to a recommendations engine tenant on an SAP Leonardo data center.
The following table summarizes the data used to generate recommendations that are transferred to the recommendations engine.
|Personalized Recommendations get derived from …||By using …|
|Learning History Data||
|User Profile Data||
|User Custom Columns||The custom column values associated with the learner|
|User Activity Data||
Enabling Personalized Recommendations
Enable Personalized Recommendations to allow users, administrators, and the Learning Management System (LMS) to make learning recommendations to learners based on their topics of interest.
Customers usually keep one learner’s security role in Learning that includes all workflows for all learners. If, however, you select workflows individually, then the user role that should access recommendations should have the recommendations workflows. For example, Access Personalized Recommendations and Access Recommendations Tile.
- Go to System Administration > Configuration >System Configuration > PERSONALIZED RECOMMENDATIONS.
- Set recommendationsEngineEnabledto true.
When you set recommendationsEngineEnabled to true, learners can receive recommendations from the recommendations engine.
- Click Apply Changes.
Learners will now be able to view Personalized Recommendations on the Find Learning page and on the Recommendations tile.
If this is the first time you are enabling the recommendations engine, then learners will be able to view the recommendations after the recommendations engine finishes processing.
Editing the Legal Disclaimer for Learning Recommendations
The disclaimer message is displayed to the users on the welcome wizard where they select topics for the first time, and it also appears on the topics subscription dialog.
Edit the legal disclaimer for learning recommendations so that when users see courses populated by the SAP SuccessFactors Learning recommendation engine, they see your legal disclaimer instead of the default disclaimer.
To enable the legal disclaimer, go to System Administration > Configuration> System Configuration> PERSONALIZED RECOMMENDATIONS and set enableDisclaimerForTheExternalContent to true.
If you are in more than one language, translate your disclaimer to the other languages so that users can read the disclaimer in their own languages and dialects.
- Go to SAP SuccessFactors Learning administration and then go to References>Manage Labels> Labels.
- In Label ID, type Recommendation.DisclaimerMessageand then click Search.
- Edit the label and then click Locales.
- For each locale, paste the disclaimer for that locale language.
- Click Apply Changes.
Assigning Workflow to a User Role to access Personalized Recommendations
To enable learners to access Personalized Recommendations you must assign Workflow to a User Role.
- Go to SAP SuccessFactors Learning administration, and then go to System Administration>Security> Role Management.
- Search for the user role for which you need to define a workflow.
- Edit the role and click on Permissions> Learning.
- To enable the recommendations tiles for a learner, select the option Access Recommendations Tile.
- To make Personalized Recommendations appear on the recommendations tile for a learner, select the option Access Personalized Recommendations.
- To save you changes click Apply Changes.
Enable Learners to view recommendations on the Recommendations tile.
Selecting User Custom Columns to be included in Personalized Recommendations
Selecting appropriate user custom columns ensures that the machine learning algorithm uses that data and populates relevant results for personalized recommendations.
Personalized Recommendations uses user attributes to identify similar groups of users to make recommendations. Optionally, User Custom Columns can also be included to further increase the relevancy of the recommendations. You should select those User Custom Columns that enables the identification of similar groups of users. The selected User Custom Columns must be different from the ones already included from the standard user attributes.
You can select the User Custom Columns by following the steps stated below.
- Go to SAP SuccessFactors Learning administration and then go to System Administration>Custom Fields > User.
- Enter a value for each field that you want to use to filter your search and click Search.
- From the search results edit the custom columns you want to select. You must select the Include in personalized recommendations option for the recommendation engine to use the value for suggesting personalized recommendations to users.
Learner data in the selected custom columns will be sent to the recommendations engine with the scheduled extracts.
SAP SuccessFactors LMS personalized learning recommendation recognizes learning similar to the recommendations from web stores, streaming services, and online publications.
By enabling personalized learning employees help maximize relevance by indicating topics of interest, which focuses the recommendations engine on delivering best-fit learning recommendations that match their preferences.
Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Instead of clicking through a lot of pages to find the right course, this pre-selection aims to exclude irrelevant and uninteresting courses so that the ones displayed are the ones most suited to the user.