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.

翻译：本研究探讨了动态用户画像嵌入对社交网络中个性化情境感知体验的影响。我们在包含超过两千万数据点的数据集上对多语言及英语Transformer模型进行了对比分析。该分析涵盖广泛的度量指标与性能参数，以比较动态画像嵌入与非嵌入（实为静态画像嵌入）的效果。通过使用退化函数进行了对比研究。大量测试与研究证实，动态嵌入能够成功追踪用户不断变化的兴趣与偏好，从而提供更精准的推荐并实现更高的用户参与度。这些发现对于旨在通过相关功能与复杂推荐引擎提升用户体验的社交媒体平台具有重要意义。