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

Sai Puppala, Ismail Hossain, Md Jahangir Alam, Sajedul Talukder

TL;DR

This work tackles private, context-aware content recommendation on social media by avoiding raw data sharing through federated learning. It trains a global GPT-based system using locally stored user data, enabling personalized filtering and real-time querying. Theoretical contributions include a federated optimization objective $\mathcal{L}(\mathbf{w}) = \sum_{k=1}^{K} \frac{n_k}{N} \mathcal{L}_k(\mathbf{w}; \mathcal{D}_k)$ with local updates and a convergence result under $0<\eta<2/L$, and practically, persona-driven content scoring, social circle analysis, smart video retrieval via a Knowledge Graph in Neo4j, and an adaptive feedback loop for continual improvement. The work demonstrates privacy-preserving, scalable personalization with potential impact on engagement and user trust in digital platforms.

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.

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

TL;DR

This work tackles private, context-aware content recommendation on social media by avoiding raw data sharing through federated learning. It trains a global GPT-based system using locally stored user data, enabling personalized filtering and real-time querying. Theoretical contributions include a federated optimization objective with local updates and a convergence result under , and practically, persona-driven content scoring, social circle analysis, smart video retrieval via a Knowledge Graph in Neo4j, and an adaptive feedback loop for continual improvement. The work demonstrates privacy-preserving, scalable personalization with potential impact on engagement and user trust in digital platforms.

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.

Paper Structure

This paper contains 24 sections, 19 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Methodologies
  4. Federated Learning Framework
  5. Convergence Proof of Context Fed Learning
  6. User Profiling and Persona Analysis
  7. User Interaction Data Collection
  8. Content Score Calculation
  9. Social Circle Analysis
  10. Smart Video Analysis
  11. Smart Video Suggestions
  12. Smart Video Query
  13. Content Filtering
  14. Readability Score ($R$)
  15. Adaptive Feedback Loop
  16. ...and 9 more sections

Figures (4)

  • Figure 1: The architecture of the Adaptive Content Filtering System, highlighting key components such as GPT, User Persona profiles, and category-based social engagement. It also provides an overview of the unique features of the federated learning global server for context-based GPT generation.
  • Figure 2: The architecture of the adaptive content filtering system illustrates the workflow, highlighting the transition from database management to user feedback collection.
  • Figure 3: The user interface of proposed system for adaptive content filtering and smart video suggestions.
  • Figure 4: The survey results feature three graphs: user content interest, overall system rating, and post relevance based on user preferences.