User Embeddings

User embeddings are vector representations of users derived from their online activities, aiming to capture individual preferences and behaviors for personalized applications. Current research emphasizes developing robust and efficient embedding methods using transformer-based architectures, graph neural networks, and large language models, often incorporating multimodal data (text, interactions, clickstreams) and addressing challenges like popularity bias and dynamic user preferences. These advancements significantly improve the performance of recommendation systems, personalized content delivery, and social network analysis, impacting both industrial applications and fundamental research in user modeling. The field is also actively exploring privacy-preserving techniques for embedding generation and utilization.