Open AccessCollaborative Filtering Recommendation of Online Learning Resources Based on Knowledge Association Model
Author(s) -
Henan Jia,
Lei Yang,
Bo Cui
Publication year - 2022
Publication title -
international journal of emerging technologies in learning/international journal: emerging technologies in learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.454
H-Index - 24
eISSN - 1868-8799
pISSN - 1863-0383
DOI - 10.3991/ijet.v17i02.29013
Subject(s) - computer science , collaborative filtering , information overload , recommender system , association (psychology) , similarity (geometry) , information retrieval , association rule learning , resource (disambiguation) , semantic similarity , knowledge management , world wide web , machine learning , artificial intelligence , computer network , philosophy , epistemology , image (mathematics)
Online learning platforms are prone to information overload, as they contain a huge number of diverse resources. To solve the problem, domestic and foreign scholars have focused their attention on personalized recommendation of learning resources. However, the existing studies perform poorly in the prediction of online learning paths, failing to clarify the overall knowledge system of students and the associations of resource knowledge. Therefore, this paper explores the collaborative filtering recommendation (CFR) of online learning resources (OLRs) based on knowledge association model. Firstly, the knowledge units were extracted from the semantic information of OLRs, and a knowledge association model was established for OLR recommendation. Next, a CFR algorithm was designed to couple semantic adjacency with learning interest, and used to quantify the semantic similarity of OLRs. The proposed algorithm was proved effective through experiments.