用规则调整知识图谱，防止大模型推荐陷入信息茧房
Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
- 通过符号规则重写用户知识图谱，抑制过度个性化特征
- 在食谱推荐任务中提升内容新颖性，同时保持推荐质量
- 适合关注推荐多样性与公平性的系统设计者
我们提出一种轻量级神经符号框架，通过在推理时调整用户侧知识图谱（KG），缓解基于大语言模型的推荐系统中的过度个性化问题。该方法不依赖模型重训练或黑箱启发式规则，而是重构用户的个性化知识图谱（PKG），以抑制强化个性化信息环境（PIEs）的特征共现模式——即算法诱导的信息茧房，从而限制内容多样性。经调整的PKG用于构建结构化提示，引导语言模型生成更具多样性的非PIE推荐结果，同时保留主题相关性。我们引入一系列符号化适应策略，包括软重加权、硬反转和有偏三元组的定向移除，并设计客户端学习算法，按用户优化策略应用。在食谱推荐基准测试中，个性化PKG调整显著提升了内容新颖性，且优于全局调整和朴素提示方法。
原文摘要 · Abstract (English)
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.
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