Developing of A Learning Content Recommendation System  Using Collaborative Filtering Based on User Rating

Erna Piantari; Fadjrin Diraja  Muhammad; Harsa Wara  Prabawa

doi:10.59934/jaiea.v4i2.710

Authors

Erna Piantari Universitas Pendidikan Indonesia
Fadjrin Diraja Muhammad Universitas Pendidikan Indonesia
Harsa Wara Prabawa Universitas Pendidikan Indonesia

DOI:

https://doi.org/10.59934/jaiea.v4i2.710

Keywords:

recommendation system, user rating, educational data mining, collaborative filtering, learning content

Abstract

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.

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