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 GPT and Context-based Social Media LLM models, utilizing federated learning for privacy and security. Four client entities receive a base GPT-2 model and locally collected social media data, with federated aggregation ensuring up-to-date model maintenance. Subsequent modules focus on categorizing user posts, computing user persona scores, and identifying relevant posts from friends' lists. A quantifying social engagement approach, coupled with matrix factorization techniques, facilitates personalized content suggestions in real-time. An adaptive feedback loop and readability score algorithm also enhance the quality and relevance of content presented to users. Our system offers a comprehensive solution to content filtering and recommendation, fostering a tailored and engaging social media experience while safeguarding user privacy.
@article{arxiv.2408.05243,
title = {SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks},
author = {Sai Puppala and Ismail Hossain and Md Jahangir Alam and Sajedul Talukder},
journal= {arXiv preprint arXiv:2408.05243},
year = {2024}
}
Comments
This research paper is submitted to ASONAM 2024 conference on Advances in Social Networks Analysis and Mining and going to be published in Springer