Training recommendations

Bealink has different referral methods. Beyond the automated widgets, content/skills, skills/objectives, skills/positions associations can be imported or integrated in order to refine recommendations in the platform.

Recommendation by collaborative AI algorithm

Bealink LXP enhances search with AI-powered recommendations. Our hybrid recommendation algorithm, recognized by the Ministry of Education and Research, combines user behavior and contextual factors (like job title) for personalized suggestions.

Currently, the platform offers collaborative filtering, recommending content based on group usage. For example, a manager might see recommendations on management and team leadership, influenced by the behavior of other managers in their group.

Our advanced hybrid recommendation engine is under development and will soon provide even more tailored suggestions.

Recommendation blocks or automatic suggestions

In addition, the application has a number of widgets that can be displayed automatically:

• The best content of the week

• The best content of the month

• The best playlists of the week

• The best playlists of the month

• The best contributors of the week (adding or creating content)

• The best contributors of the month (adding or creating content)

• Recent content

Using the applications codes such as Spotify, Netflix or Amazon Prime, the Bealink LXP offers all these blocks in order to engage the user and provide the experience of a lively and dynamic platform.

These suggestions for content or training, among the most viewed, or contributors to follow, will not only encourage peer-learning in the organisation but also the feeling that a space is personalized. Because, on all these blocks, the user can add content to their own playlists, share content, add favourites...

These dynamic blocks therefore do not require any additional workload on the L&D side and participate in the animation of the platform.

Topics of interest

Learners can customize their profiles to receive tailored recommendations based on their interests. The platform supports hierarchical skills and topics, enabling granular personalization and targeted content delivery.

Recommendation and assignment by a manager

Humans remain invaluable in the recommendation process. Managers can directly recommend content to their teams, either by sharing it or by making training requests on their behalf. This personalized touch ensures relevance and actionability, often surpassing algorithmic suggestions.