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Updated: Jun 11, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Interaction-knowledge semantic alignment for recommendation.

Zhen-Yu He¹, Jia-Qi Lin², Chang-Dong Wang¹

¹School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.

Neural Networks : the Official Journal of the International Neural Network Society

|October 2, 2024

Summary

This summary is machine-generated.

This study introduces a new recommendation method (IKSAR) that aligns semantic differences between user interactions and knowledge graphs. This approach improves item representation by considering user preferences, outperforming existing methods.

Keywords:

Graph neural network Knowledge graph Recommendation Semantic alignment

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Area of Science:

Computer Science
Artificial Intelligence
Data Science

Background:

Recommender systems face data sparsity challenges.
Knowledge graphs enhance recommenders with item information.
Existing methods overlook semantic differences and user preferences.

Purpose of the Study:

To address limitations in knowledge graph-enhanced recommender systems.
To propose a novel recommendation method called Interaction-Knowledge Semantic Alignment for Recommendation (IKSAR).
To improve item representation by reducing semantic gaps and incorporating user preferences.

Main Methods:

Introduced a semantic alignment module to bridge interaction and knowledge graphs.
Integrated user representation during item representation fusion.
Developed the Interaction-Knowledge Semantic Alignment for Recommendation (IKSAR) method.

Main Results:

The semantic alignment module reduced differences between interaction and knowledge graphs.
User representation integration enhanced fused item representations.
IKSAR demonstrated superior performance over existing methods in experiments.

Conclusions:

IKSAR effectively overcomes limitations of current knowledge graph-enhanced recommenders.
The method shows significant improvements in recommendation quality.
IKSAR offers a promising direction for future recommender system research.