Lightweight Inference-Time Personalization for Frozen Knowledge Graph Embeddings

• URL: http://arxiv.org/abs/2512.22398v1
• Date: Fri, 26 Dec 2025 22:30:37 GMT
• Title: Lightweight Inference-Time Personalization for Frozen Knowledge Graph Embeddings
• Authors: Ozan Oguztuzun, Cerag Oguztuzun,
• Abstract summary: GatedBias is a lightweight inference-time personalization framework for knowledge graphs.<n>Profile-specific features combine with graph-derived binary gates to produce interpretable, per-entity biases.<n>We evaluate GatedBias on two benchmark datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Foundation models for knowledge graphs (KGs) achieve strong cohort-level performance in link prediction, yet fail to capture individual user preferences; a key disconnect between general relational reasoning and personalized ranking. We propose GatedBias, a lightweight inference-time personalization framework that adapts frozen KG embeddings to individual user contexts without retraining or compromising global accuracy. Our approach 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. We evaluate GatedBias on two benchmark datasets (Amazon-Book and Last-FM), demonstrating statistically significant improvements in alignment metrics while preserving cohort performance. Counterfactual perturbation experiments validate causal responsiveness; entities benefiting from specific preference signals show 6--30$\times$ greater rank improvements when those signals are boosted. These results show that personalized adaptation of foundation models can be both parameter-efficient and causally verifiable, bridging general knowledge representations with individual user needs.

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