# Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations

arXiv: https://arxiv.org/abs/2509.07133 · HTML: https://mlsanity.com/paper/2509.07133/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 8 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [Bursting Filter Bubble: Enhancing Serendipity Recommendations with Aligned Large Language Models](https://mlsanity.com/paper/2502.13539/) (2025)
- [Mitigating Filter Bubbles within Deep Recommender Systems](https://mlsanity.com/paper/2209.08180/) (2022)

## Built on by (most cited first, 3 of 3)

- [Federated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendations](https://mlsanity.com/paper/2603.13264/) (2026)
- [Aligning Language Models with Investor and Market Behavior for Financial Recommendations](https://mlsanity.com/paper/2510.15993/) (2025)
- [PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Tasks](https://mlsanity.com/paper/2608.21381/) (2026)

## Abstract (arXiv)

We present a lightweight neuro-symbolic framework to mitigate over-personalization in LLM-based recommender systems by adapting user-side Knowledge Graphs (KGs) at inference time. Instead of retraining models or relying on opaque heuristics, our method restructures a user's Personalized Knowledge Graph (PKG) to suppress feature co-occurrence patterns that reinforce Personalized Information Environments (PIEs), i.e., algorithmically induced filter bubbles that constrain content diversity. These adapted PKGs are used to construct structured prompts that steer the language model toward more diverse, Out-PIE recommendations while preserving topical relevance. We introduce a family of symbolic adaptation strategies, including soft reweighting, hard inversion, and targeted removal of biased triples, and a client-side learning algorithm that optimizes their application per user. Experiments on a recipe recommendation benchmark show that personalized PKG adaptations significantly increase content novelty while maintaining recommendation quality, outperforming global adaptation and naive prompt-based methods.