Enhancing Recommendation Accuracy and Explainability by Integrating Knowledge Graphs and Social Networks with Graph Attention

This paper proposes a knowledge-aware influence propagation recommendation model based on graph attention, integrating user–item interactions with semantic information from knowledge graphs (KGs) and neighbor relations from social networks. In semanti

Authors

Institutions

Publication Details

Published
2025-10-03
DOI
https://doi.org/10.22541/au.175952223.35212771/v1
Primary Topic
Advanced Graph Neural Networks
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing Recommendation Accuracy and Explainability by Integrating Knowledge Graphs and Social Networks with Graph Attention

Guifei Jiang, Meng Xian-tao, Yuping Yang
Advanced Graph Neural Networks
article

Enhancing Recommendation Accuracy and Explainability by Integrating Knowledge Graphs and Social Networks with Graph Attention

Guifei Jiang, Meng Xian-tao, Yuping Yang
article en

Abstract

This paper proposes a knowledge-aware influence propagation recommendation model based on graph attention, integrating user–item interactions with semantic information from knowledge graphs (KGs) and neighbor relations from social networks. In semanti

China Mobile (China) (CN), Nankai University (CN), Software (Spain) (ES)
Openalex Percentile: Top 86%
Advanced Graph Neural Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.