<![CDATA[The advancement of artificial technology has paved the way for personalized learning experiences through adaptive systems which could be built by developing a recommendation system. In education filed, a variety of learning material recommendation systems that employ user filtering algorithms has prompted a lot attention as well. These systems aim to enhance the learning journey by offering tailored learning content suggestions based on individual preferences. This research explores the design of recommendation of learning content system, focusing on user filtering algorithms to analyze user preferences. By leveraging techniques such as collaborative filtering and user-based filtering, the system can accurately predict and recommend relevant learning materials to users based on others rating. The system continuously refines itself in an effort to increase user satisfaction and recommendation accuracy, which will eventually contribute to more efficient and engaging learning experiences.]]>
Copyrights © 2025
