Real-Time Personalized Content Adaptation through Matrix Factorization and Context-Aware Federated Learning

TL;DR

This paper introduces a federated learning framework that personalizes social media content in real-time by fine-tuning language models with user data, ensuring privacy while improving relevance and engagement.

Contribution

It presents a novel combination of federated learning, matrix factorization, and context-aware models for personalized content recommendation in social media.

Findings

Enhanced content relevance and user engagement.

Effective privacy-preserving personalization.

Improved content filtering and recommendation quality.

Abstract

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.