Enhancing Social Media Personalization: Dynamic User Profile Embeddings and Multimodal Contextual Analysis Using Transformer Models

TL;DR

This paper demonstrates that dynamic user profile embeddings, combined with multimodal contextual analysis using transformer models, significantly improve personalized social media experiences by better tracking user preferences and increasing engagement.

Contribution

It introduces a novel approach of dynamic profile embeddings with transformer models for social media personalization, outperforming static profiles in accuracy and user engagement.

Findings

Dynamic embeddings better track changing user preferences.

Enhanced recommendations lead to higher user engagement.

Transformer models improve contextual understanding in social media.

Abstract

This study investigates the impact of dynamic user profile embedding on personalized context-aware experiences in social networks. A comparative analysis of multilingual and English transformer models was performed on a dataset of over twenty million data points. The analysis included a wide range of metrics and performance indicators to compare dynamic profile embeddings versus non-embeddings (effectively static profile embeddings). A comparative study using degradation functions was conducted. Extensive testing and research confirmed that dynamic embedding successfully tracks users' changing tastes and preferences, providing more accurate recommendations and higher user engagement. These results are important for social media platforms aiming to improve user experience through relevant features and sophisticated recommendation engines.