通过联邦学习实现社交内容实时个性化推荐，保护隐私。
Real-Time Personalized Content Adaptation through Matrix Factorization and Context-Aware Federated Learning
- 基于联邦学习与矩阵分解，本地微调GPT模型。
- 结合用户画像与好友网络，实现实时内容推荐。
- 适合关注隐私保护的社交平台开发者。
本研究提出一种多维度方法，通过联邦学习框架提升社交媒体平台的用户互动与内容相关性。多个客户端接收基础GPT模型，利用本地收集的社交媒体数据进行微调，同时通过联邦聚合保障数据隐私。核心模块包括用户生成内容分类、用户人格评分计算以及好友网络中相关帖子识别。通过将复杂的社交参与度量化方法与矩阵分解技术结合，系统可实时提供个性化内容推荐。此外，自适应反馈回路与鲁棒的可读性评分算法显著提升了呈现内容的质量与相关性。该方案不仅解决内容过滤与推荐挑战，还增强社交体验，同时保护用户隐私，为数字平台个性化交互树立新标准。
原文摘要 · Abstract (English)
Our study presents a multifaceted approach to enhancing user interaction and content relevance in social media platforms through a federated learning framework. We introduce personalized LLM Federated Learning and Context-based Social Media models. In our framework, multiple client entities receive a foundational GPT model, which is fine-tuned using locally collected social media data while ensuring data privacy through federated aggregation. Key modules focus on categorizing user-generated content, computing user persona scores, and identifying relevant posts from friends networks. By integrating a sophisticated social engagement quantification method with matrix factorization techniques, our system delivers real-time personalized content suggestions tailored to individual preferences. Furthermore, an adaptive feedback loop, alongside a robust readability scoring algorithm, significantly enhances the quality and relevance of the content presented to users. This comprehensive solution not only addresses the challenges of content filtering and recommendation but also fosters a more engaging social media experience while safeguarding user privacy, setting a new standard for personalized interactions in digital platforms.
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