Our unique recommender system helps learners stay up-to-date on their topics and continue to develop. We compare learner data with new articles, studies, and courses found on deposited websites, internal company sources, or optionally on the internet in general.
What Are Recommender Systems?
A recommendation system, also called a recommender system, is a software solution that makes personalized suggestions or recommendations to users for products, services, or content — based on the user's preferences, behavior and interactions. Such systems are often used in online stores, streaming platforms, and social networks, but also on modern learning platforms like chunkx.
On traditional learning platforms, they are usually limited to recommending new courses from the platform itself or a limited number of partners. We think this can be done better.
Recommender Systems in chunkx
In chunkx, we not only process which courses a learner is taking, but we can directly analyze the content of the many individual learning units. This allows us to make comparisons in terms of content. For example, if you learn a lot about diverse leadership, we can use the content of your learning units and your learning data to match them with skills and generate appropriate recommendations:
1) Matching courses in chunkx
A very important feature for our customers who use our microlearning platform.
2) Suitable courses outside of chunkx
Using AI and well-placed data extraction, we can find suitable courses for you to continue learning — taking skills, development goals and other parameters into account.
3) Articles, studies and updates
chunkx already supports you intensively in the transfer of learning. With our recommender system, we also make it easy for you to benefit from new articles, studies, and general updates on your topics. As long as you keep the subscription to the channel, we will automatically compare your learning data and skills with newly found items and let you know as soon as we find something suitable for you.
Recommendation Sources
Quality is a core requirement for our automatically generated recommendations. The first step is to control the sources from which new recommendations may be generated:
Selected sources: Together with our customers, we determine which sources they want us to crawl on a regular basis. This ensures that only sources requested by the customer are considered. Crawling means that a robot revisits the website regularly and analyzes what has been newly published. This content is vectorized to make it easier to compare with learning data.
In-house sources: Customers have even more control over their internal data. Similar to the approach above, we vectorize internal data — such as a product database or knowledge platform — and respond to identified knowledge gaps with the perfect recommendations on internal articles.
The free internet: Especially for topics that are highly topical, such as Generative AI, where things can change on a daily basis, we recommend that customers refrain from restricting sources. Without this restriction, we can find even more suitable recommendations for courses, articles and updates.
Validation of Recommendations
With innovative automation, careful validation is essential. All results that are content-wise close enough end up on a shortlist. We then have the shortlist analyzed with GPT-4 and compared again with the learner data. We only use services via Microsoft Azure, so all data is processed in Europe (specifically in France).
The top results are checked by a human before publication, so that nothing can go wrong.
Linking with the Next Learning Unit
Every communication event is used by chunkx to send learners the next learning unit. In this way, we support continuous learning and enable learning directly in the flow of work — in the channels that people already use anyway (currently email and MS Teams).
Talk to us about your learning culture and the changes in your organization, and how chunkx can best support you.
