The field of personalized recommendation systems is moving towards more sophisticated and nuanced approaches to understanding user preferences and behavior. Researchers are developing innovative methods to debias watch time prediction in video recommendation platforms, such as relative advantage debiasing and multi-granularity distribution modeling. Additionally, there is a growing interest in using recommendation systems to promote sustainable choices and healthy behaviors. Noteworthy papers in this area include: Relative Advantage Debiasing for Watch-Time Prediction in Short-Video Recommendation, which proposes a novel framework for correcting watch time biases, and Personalized Recommendations via Active Utility-based Pairwise Sampling, which introduces a generalized utility-based framework for learning preferences from pairwise comparisons. Furthermore, researchers are exploring new approaches to approximate unlearning in session-based recommendation, such as Curriculum Approximate Unlearning, which addresses the challenges of applying gradient ascent to session-based recommendation. Overall, the field is advancing towards more accurate, data-efficient, and user-centric recommendation systems.