The field of recommendation systems has seen significant advancements aimed at enhancing model stability, capturing nuanced user behaviors, and addressing data sparsity challenges. A common theme across recent research is the integration of sophisticated techniques to improve both the performance and robustness of recommendation systems. Innovations such as supervised learning-enhanced actor-critic frameworks for live-stream and video recommendations have demonstrated improved long-term user engagement while maintaining model stability. Additionally, there has been a notable shift towards modeling user intent more comprehensively, leveraging multi-graph co-training and session-based approaches to better predict user interactions. The use of unsupervised learning techniques, such as autoencoders, has also been explored to enhance the conformal predictability of context-aware recommendation systems. Notably, cross-domain recommendation strategies employing disentangled contrastive learning have been effective in addressing the cold-start problem by avoiding negative transfer and improving recommendation accuracy. Furthermore, the adoption of recursive frameworks and sequential recommendation systems, enhanced with future data and enduring hard negatives, has showcased state-of-the-art performance across various benchmarks. Transformer-based models are being augmented with frequency information to better handle next-basket recommendation tasks, demonstrating substantial improvements in recall metrics. These developments collectively aim to create more personalized, stable, and efficient recommendation systems that cater to diverse user needs and behaviors. Noteworthy contributions include the Supervised Learning-enhanced Multi-Group Actor Critic algorithm for live-stream recommendation and a novel user model that accounts for dual-self behavior to optimize enrichment in recommendation strategies.