Next Article in Journal
Less Is More: Principled Diversity in Heterogeneous Anomaly Detection Ensembles
Previous Article in Journal
Non-Invasive Blood Glucose Estimation from Exhaled Breath: Patient-Level Validation of a Compact Electronic Nose Approach
Previous Article in Special Issue
MoHyNet: Enhancing Session-Based Recommendations via Hypergraph Motifs and Contrastive Learning
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
AI
Volume 7
Issue 6
10.3390/ai7060215
ai-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Knowledge-Aware Recommendation Based on Hypergraph and Knowledge Graph

by
Shunping Niu
,
Kuo Chi
*,
Ting Su
,
Yongqin Yang
and
Jiabao Gao
School of Information and Communication Engineering, Hainan University, Haikou 570228, China
*
Author to whom correspondence should be addressed.
AI 2026, 7(6), 215; https://doi.org/10.3390/ai7060215
Submission received: 22 April 2026 / Revised: 7 June 2026 / Accepted: 8 June 2026 / Published: 11 June 2026
(This article belongs to the Special Issue AI for Recommendation Systems and Their Applications)
Versions Notes

Abstract

Conventional recommender systems often rely on shallow collaborative signals, which limits their performance under sparse and popularity-skewed conditions. To address this, we propose a knowledge-aware framework that combines an item hypergraph induced by user interaction histories, a top-k user similarity graph, and one-hop, relation-aware knowledge-graph aggregation. The hypergraph branch learns high-order item co-occurrence representations, which are aggregated into initial user vectors and then refined through user similarity propagation. On the item side, user-conditioned relation attention aggregates one-hop KG neighbors to produce semantic item representations. User and item representations are fused by an MLP scorer, and a lightweight popularity-aware post-scoring adjustment can optionally be applied to moderate head-item dominance. Experiments on MovieLens-1M, Last.FM and Book-Crossing show strong performance among the compared baselines in AUC, ACC, and Recall@K.
Keywords: recommender systems; hypergraph modeling; knowledge graph recommender systems; hypergraph modeling; knowledge graph

Share and Cite

MDPI and ACS Style

Niu, S.; Chi, K.; Su, T.; Yang, Y.; Gao, J. Knowledge-Aware Recommendation Based on Hypergraph and Knowledge Graph. AI 2026, 7, 215. https://doi.org/10.3390/ai7060215

AMA Style

Niu S, Chi K, Su T, Yang Y, Gao J. Knowledge-Aware Recommendation Based on Hypergraph and Knowledge Graph. AI. 2026; 7(6):215. https://doi.org/10.3390/ai7060215

Chicago/Turabian Style

Niu, Shunping, Kuo Chi, Ting Su, Yongqin Yang, and Jiabao Gao. 2026. "Knowledge-Aware Recommendation Based on Hypergraph and Knowledge Graph" AI 7, no. 6: 215. https://doi.org/10.3390/ai7060215

APA Style

Niu, S., Chi, K., Su, T., Yang, Y., & Gao, J. (2026). Knowledge-Aware Recommendation Based on Hypergraph and Knowledge Graph. AI, 7(6), 215. https://doi.org/10.3390/ai7060215

Article Metrics

Back to TopTop