用智能代理实现骚扰内容的个性化过滤，更准更贴心。
Agent-Based User-Adaptive Filtering for Categorized Harassing Communication
- 每个用户配一个自适应代理，动态调整容忍度。
- 相比固定规则，准确率和用户满意度显著提升。
- 适合需要保护隐私又想自主控制的社交平台用户。
我们提出一种基于智能体的框架，用于在线社交网络中对分类骚扰通信进行个性化过滤。与应用统一规则的全局审核系统不同，该方法通过自适应过滤代理建模用户的特定容忍度和偏好。这些代理根据用户反馈学习，并在多个骚扰类别（包括冒犯性、攻击性和仇恨内容）中动态调整过滤阈值。我们采用监督分类技术并使用模拟用户交互数据实现并评估该框架。实验结果表明，相较于静态模型，自适应代理能显著提高过滤精度和用户满意度。所提出的系统展示了基于智能体的个性化如何在维护用户自主权的同时增强内容审核效果。
原文摘要 · Abstract (English)
We propose an agent-based framework for personalized filtering of categorized harassing communication in online social networks. Unlike global moderation systems that apply uniform filtering rules, our approach models user-specific tolerance levels and preferences through adaptive filtering agents. These agents learn from user feedback and dynamically adjust filtering thresholds across multiple harassment categories, including offensive, abusive, and hateful content. We implement and evaluate the framework using supervised classification techniques and simulated user interaction data. Experimental results demonstrate that adaptive agents improve filtering precision and user satisfaction compared to static models. The proposed system illustrates how agent-based personalization can enhance content moderation while preserving user autonomy in digital social environments.
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