The resource recommendation services aim to provide suggestions of learning resources to users in Didactalia. These recommendations are calculated based on the identified and extracted features indicative of learning activities such as past user interactions, resource popularity and resource content. Three use cases have been identified that are of interest for Didactalia:
- The recommendation of popular resources in the system (i.e., non-personalized).
- The recommendation of resources with whom like-minded users have interacted (i.e, personalized).
- The recommendation of (alternative) similar resources for a specific resource (i.e., more-like-this).
The first use case was realized using a MostPopular algorithm. This approach recommends items which are weighted and ranked by the number of interactions (see Figure 1). Therefore, the Most Popular approach is non personalized and thus, every user would receive the same recommendation. The approach can be refined on the use case where certain interactions are weighted differently. For example, if a person rated a resource compared to just looking at it.
Figure 1. Popular resources in Didactalia.