Maximizing User Satisfaction with Machine Learning-Powered Movie Recommender Systems

The goal of recommendation systems is to provide customers with practical and sensible recommendations for products or goods they might be interested in. A recommendation engine extracts data and, using a variety of techniques, suggests to customers the most important stuff. Content-driven filtering (CF), item-based collaborative filtering (IBCF), and the K-Nearest algorithm (KNN) are movie recommendation strategies used in this study. These methods make an effort to filter users' preferences using the data collected and present movies based on that profile. The MovieLens dataset is used by all three algorithms to produce the Cosine Similarity index. Both cold start capabilities and the issue of data sparsity are addressed. For a sample of 910 films, the actual and expected ratings are displayed using Tableau visualization tools. Further precision is calculated when the evaluation assessment using Root Mean Square Error (RMSE) is completed. According to the experimental findings, item-based collaborative filtering, out of the three algorithms, produces the best results with the least amount of mistake and the greatest degree of precision (84.9%).