Filtering-Based Movie Recommendation System: A Comparative Study

Predicting consumer behavior patterns, considering the state of the online retail and streaming industries, is the aim of making a suitable product recommendation. When it comes to films and television shows, the language in which they are produced, the members of the cast and crew, and, most importantly, the genre in which they are set, can all be significant deciding factors. The client’s behavioral habits and demographic background are quite significant. The study model presented in this paper anticipates customers’ behavior patterns based on their demographic data and past behavior patterns by utilizing a few recommenders’ system-based methodologies. After investigating several machine learning algorithms based on recommender systems, the model was created. On our dataset, model-based collaborative filtering gave a better result than the other techniques used, with an accuracy of 99.2% and precision of 99.6%. The major goal has been met with the recommended model’s ability to outperform the existing works stated in the related work section.