Enhancing Social Media Personalization: Dynamic User Profile Embeddings and Multimodal Contextual Analysis Using Transformer Models
This work addresses the need for better personalization on social media platforms to enhance user experience, though it appears incremental as it builds on existing transformer and embedding methods.
This study tackled the problem of improving personalized experiences in social networks by comparing dynamic versus static user profile embeddings using transformer models on a dataset of over 20 million data points. The results showed that dynamic embeddings successfully track changing user preferences, leading to more accurate recommendations and higher user engagement.
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.