The field of user embeddings and cross-market recommendations is witnessing significant advancements, driven by the integration of large language models, graph representation learning, and innovative encoding techniques. Researchers are exploring new ways to derive high-quality user embeddings from event sequences, leveraging techniques such as next-token prediction, text enrichment, and contrastive learning. These advancements have the potential to improve the accuracy and robustness of user classification tasks, recommendation systems, and other downstream applications. Notably, the use of market-specific and market-shared insights is enhancing the generalizability and robustness of cross-market recommender systems. Furthermore, the development of universal user representations that capture essential aspects of user behavior is reducing the need for task-specific feature engineering and model retraining, leading to more scalable and efficient machine learning pipelines. Noteworthy papers include: LLM4ES, which achieves state-of-the-art performance in user classification tasks, and Encode Me If You Can, which proposes a method for learning universal user representations via event sequence autoencoding. LATTE is also notable for its contrastive learning framework that aligns raw event embeddings with semantic embeddings from frozen LLMs, significantly reducing inference cost and input size.