Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
This addresses the problem of algorithmic filter bubbles in recommender systems for users, though it is an incremental improvement combining existing neuro-symbolic techniques.
The paper tackles over-personalization in LLM-based recommender systems by adapting user-side Knowledge Graphs at inference time to suppress feature co-occurrence patterns that reinforce filter bubbles, resulting in significantly increased content novelty while maintaining recommendation quality on a recipe benchmark.
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.