TITLE:
Research on Personalized Resource Recommendation Based on User Profile and Collaborative Filtering Algorithm
AUTHORS:
Dongxu Liu
KEYWORDS:
Online Learning, Personalized Recommendation, Collaborative Filtering, User Profile, K-Means Algorithm
JOURNAL NAME:
Advances in Applied Sociology,
Vol.13 No.11,
November
22,
2023
ABSTRACT: With the flourishing development of online education,
the problem of information overload in learning resources is becoming
increasingly prominent. This study proposes a learning resource recommendation
method that combines user profiling and collaborative filtering algorithms. It
involves acquiring both static and dynamic user data from an online learning
platform, constructing a user profile label library, conducting user group
clustering using the K-means algorithm, calculating user similarity within each
group and identifying the most similar users to the target user, ultimately generating the
resource recommendation list based on the learning preferences of these similar
users. Personalized recommendations for learning resource are of significant
importance for improving learning effectiveness on online learning platforms,
enhancing user satisfaction, and promoting the development of personalized
education.