Traditional recommenders struggle with cold-start users and short-term context. Contextual Multi-Armed Bandits (CMAB) continuously learns online, balancing exploitation and exploration based on real-time context. In Part 1, we build a Python prototype to simulate user behavior and validate the algorithm, laying the groundwork for scalable, real-time recommendations.