Enhancing Dynamic Movie Recommendations With User Expectation Ratings in Contextual Bandit Models

Modern movie recommendation systems face challenges such as dynamic personalization and real-time adaptability. Traditional methods like collaborative filtering and content-based recommendations struggle with dynamic user preferences and cold-start problems. Contextual multi-armed bandit (CMAB) algorithms offer a promising solution by balancing the exploration and exploitation trade-off while incorporating contextual information. This paper evaluates the performance of CMAB algorithms in dynamic movie recommendation scenarios using the MovieLens Beliefs Dataset 2024. The study employs Linear Upper Confidence Bound (LinUCB) and Contextual Thompson Sampling (CTS) algorithms, incorporating user historical data and expected ratings as context. Results show that CMAB algorithms significantly outperform traditional multi-armed bandits (MAB) in terms of cumulative regret and rating accuracy. Specifically, LinUCB achieves rating accuracies of 0.712, compared to 0.623 for the Upper Confidence Bound (UCB) algorithm, indicating a 10% improvement. The enhanced context with user expectation ratings further improves recommendation performance. This research demonstrates the effectiveness of CMAB algorithms in dynamic environments and provides insights for future recommendation system designs.