Paper#Knowledge Graph, Personalization, Recommendation Systems, Machine Learning🔬 ResearchAnalyzed: Jan 3, 2026 20:05
Lightweight Personalization for Knowledge Graph Embeddings
Published:Dec 26, 2025 22:30
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of personalizing knowledge graph embeddings for improved user experience in applications like recommendation systems. It proposes a novel, parameter-efficient method called GatedBias that adapts pre-trained KG embeddings to individual user preferences without retraining the entire model. The focus on lightweight adaptation and interpretability is a significant contribution, especially in resource-constrained environments. The evaluation on benchmark datasets and the demonstration of causal responsiveness further strengthen the paper's impact.
Key Takeaways
- •Proposes GatedBias, a lightweight framework for personalizing frozen KG embeddings.
- •Achieves personalization without retraining the base model, saving computational resources.
- •Employs structure-gated adaptation for interpretable, per-entity biases.
- •Demonstrates statistically significant improvements in alignment metrics while preserving cohort performance.
- •Validates causal responsiveness through counterfactual perturbation experiments.
Reference
“GatedBias introduces structure-gated adaptation: profile-specific features combine with graph-derived binary gates to produce interpretable, per-entity biases, requiring only ${\sim}300$ trainable parameters.”