Tackling the cold start issue in movie recommendations with a refined epsilon-greedy approach

With the rapid growth of the Internet and the consequent surge in data, the current era is characterized by information overload. As the domain of data processing and storage expands, recommendation systems have become pivotal tools in navigating this deluge, assisting users in filtering through vast information landscapes. A notable segment of this is movie recommendation systems. As living standards rise, so does the demand for cinematic experiences. Enhancing and refining the methodologies of these recommendation systems is, therefore, of significant value. However, a consistent challenge is the cold start problem encountered when new users join. Without prior viewing records or preferences, these users pose a dilemma for the system: how to offer relevant recommendations without historical data? Addressing this challenge, this paper proposes a unique method grounded in the N-armed bandit model, introducing an enhanced Epsilon-greedy algorithm specifically designed for movie recommendations for such users. By adjusting dynamically based on real-time user feedback, the algorithm aims to continuously hone its recommendation quality, ensuring a consistently better user experience.