The field of smart manufacturing and recommendation systems is witnessing a significant shift towards intelligent decision making, driven by the integration of Large Language Models (LLMs) and other advanced technologies. Researchers are exploring the potential of LLMs to enhance control in steel production, improve recommendation systems, and develop more efficient and effective decision-making frameworks. A key direction in this area is the development of hybrid approaches that combine the strengths of data-driven models and knowledge-driven LLMs to solve complex problems. Noteworthy papers in this regard include Smart Manufacturing: MLOps-Enabled Event-Driven Architecture for Enhanced Control in Steel Production, which proposes a digital twin-based approach for smart manufacturing, and LLM Reasoning for Cold-Start Item Recommendation, which utilizes LLMs to effectively infer user preferences in cold-start scenarios. Additionally, papers such as Token-Controlled Re-ranking for Sequential Recommendation via LLMs and Multimodal Large Language Models with Adaptive Preference Optimization for Sequential Recommendation are also making significant contributions to the field. These developments are paving the way for more intelligent, efficient, and effective decision-making systems in smart manufacturing and recommendation systems.