CKACL: A Collaborative Knowledge-Aware Contrast Learning Framework for Addressing Sparsity and Cold-Start Problems in Recommendation System

CKACL: A Collaborative Knowledge-Aware Contrast Learning Framework for Addressing Sparsity and Cold-Start Problems in Recommendation System

Knowledge graphs have emerged as a powerful tool for enhancing recommendation systems by encoding rich side information to model user preferences with improved accuracy. Despite their potential, most knowledge graph-based recommendation systems still face two persistent challenges: data sparsity and...

Full description

Saved in:
Bibliographic Details
Main Authors: Xintao Ma, Shuai Wu, Hao Zhang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Knowledge graph recommendation
contrastive learning
cold-start problem
data sparsity
collaborative filtering
graph neural networks
Online Access:https://ieeexplore.ieee.org/document/11113308/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/11113308/

Similar Items