Traditional recommendation systems often struggle with cold-start users and with incorporating immediate contextual signals. In contrast, Contextual Multi-Armed Bandits, or CMAB, learn continuously in an online setting by balancing exploration and exploitation using real-time context. In Part 1, we develop a Python prototype that simulates user behavior and validates the algorithm, establishing a foundation for scalable, real-time recommendation systems.