  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.
