Application of Conditional Random Field Model Based on Machine Learning in Online and Offline Integrated Educational Resource Recommendation

It is of great significance to mine the learning resources that learners are interested in from massive data and recommend appropriate educational resources to them according to the characteristics of students. To improve the accuracy of educational resource recommendation, this paper proposes an ed...

Full description

Saved in:
Bibliographic Details
Published in:Mathematical problems in engineering Vol. 2022; pp. 1 - 9
Main Author: Zeng, Erqi
Format: Journal Article
Language:English
Published: New York Hindawi 24.06.2022
John Wiley & Sons, Inc
Subjects:
Algorithms
Collaboration
Conditional random fields
Deep learning
Distance learning
Education
Graph representations
Inference
Keywords
Machine learning
Neural networks
Online instruction
Ontology
Preferences
Recommender systems
Semantic web
Semantics
ISSN:1024-123X, 1563-5147
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:It is of great significance to mine the learning resources that learners are interested in from massive data and recommend appropriate educational resources to them according to the characteristics of students. To improve the accuracy of educational resource recommendation, this paper proposes an educational resource recommendation system based on a graph attention network and conditional random field fusion model. It builds all comments for each student and educational resource into a comment graph. Through the graph's topological structure to capture the network and the dependency between words in the commentary text, the adjacency information of each node is aggregated by the graph attention network based on connection relation. After the graph attention network layer, the conditional random field inference layer is added. The label sequence with the highest probability is output by the dependent random field inference layer, which is taken as the final recommendation result of the model. Experimental results show that the proposed algorithm has better performance in accuracy and diversity than the traditional recommendation algorithm.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/5746671