Design of Personalized Recommendation System for College Education Based on Multivariate Hybrid Criteria Fuzzy Algorithm

The learner model's design is a most important aspect of a personalized college student education recommendation system. Currently, most learner models need more scientific focus, relying on a single method to collect dimensions and feature attributes with low computing costs. Hence, this paper...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of Electrical Systems Jg. 20; H. 6s; S. 555 - 565
1. Verfasser: Yang, Mengxi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Paris Engineering and Scientific Research Groups 29.04.2024
Schlagworte:
Algorithms
Artificial intelligence
Clustering
College students
Colleges & universities
Criteria
Customization
Distance learning
Education
Educational materials
Educational technology
Feedback
Higher education
Methods
Multivariate analysis
Optimization algorithms
Personalized learning
Recommender systems
Sentiment analysis
Teaching
ISSN:1112-5209
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The learner model's design is a most important aspect of a personalized college student education recommendation system. Currently, most learner models need more scientific focus, relying on a single method to collect dimensions and feature attributes with low computing costs. Hence, this paper introduced a Multivariate Hybrid Criteria Fuzzy Algorithm (MHCFA) for personalized college education recommendation. The MHCFA is trained by Social Feedback Artificial Tree (SFAT), where SFAT is the combination of Social Optimization Algorithm (SOA) and Feedback Artificial Tree (FAT). In addition, Deep Fuzzy Clustering (DFC) is utilized to group college education content. The RV-Coefficient is employed to select the best content. Moreover, the feature is extracted by All Caps and numerical for further personalized recommendations. In addition, the test results show that SFATMHCFA performs better in Precision, Recall, and F-Measure where the values gained 0.989, 0.878, and 0.859, respectively.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1112-5209
DOI:10.52783/jes.2694