The online learning space and resources have been growing exponentially and have produced a challenge in the form of information overload. To overcome this challenge, recommendation systems that have the ability to recommend actions, learning resources or links for users, according to their interests, have been proposed. Recommender systems can influence and guide users in a personalized way to objects of interest from a large space of available resources. Items similar to those searched, clicked or liked in the past by a given user are recommended by content-based recommender systems. This chapter aims to present the implementation of a course recommendation system using a content-based filtering approach. The web app created uses the features and courses looked up by the users in order to recommend them with courses that they might like. It uses the information provided by the user, and it is able to gather similar ones from the database on the basis of which it curates recommendations.
|Title of host publication||Recommender Systems|
|Subtitle of host publication||A Multi-Disciplinary Approach|
|Number of pages||24|
|Publication status||Published - 1 Jan 2023|
ASJC Scopus subject areas
- Computer Science (all)
- Mathematics (all)