Application of collaborative filtering in movie recommendation systems and improvements by hyperparameter tuning

In the current era of explosive data growth, accurately recommending movies to users has become a challenge for traditional recommendation algorithms. In this paper, we propose enhancements to the traditional item-based Collaborative Filtering recommendation algorithm by focusing on three aspects: the proportion of the training set and test set, the new similarity algorithm, and the new recall index. These enhancements aim to achieve better recommendation results. We conducted experiments using a movie recommendation system as the testbed and implemented an item-based recommendation algorithm using the Python language. A control experiment was performed using the dataset from the official MovieLens website. The experimental results demonstrate that the improved algorithm exhibits enhanced recommendation accuracy.