The field of personalized recommendations is shifting towards the integration of Large Language Models (LLMs) to improve the accuracy and transparency of recommendation systems. Recent developments focus on addressing the cold-start problem, where new items or users lack historical data, and incorporating user preferences and behaviors into LLM-based recommenders. Researchers are also exploring the use of cognitive architectures, such as ACT-R, to simulate human decision-making and provide more human-centered recommendations. Notable advancements include the development of Memory-Assisted LLMs, which capture diverse user preferences and account for timely updates to user history. The use of prompt-based LLMs for position bias-aware reranking is also being investigated, although initial results highlight the limitations of LLMs in modeling ranking context and mitigating bias. Noteworthy papers include: The paper proposing the Memory-Assisted Personalized LLM, which demonstrates improved performance over regular LLM-based recommenders, especially as user history grows. The paper introducing a hybrid framework combining traditional recommendation models with LLMs for reranking, which reveals the limitations of LLMs in mitigating position bias.