Hybrid Movie Recommendation System Based on User Preferences and Item Similarity

In recent years, recommendation systems have gained popularity for providing users with relevant information to aid them in decision-making from vast amounts of available data. Movie recommendation systems rely on either user similarity (collaborative filtering) or specific user preferences (content-based filtering) to generate recommendations. Often, both methods are combined to enhance the effectiveness of recommendation systems, alongside various similarity measures to determine user likeness for recommendations. This study investigates current techniques, including content-based filtering, collaborative filtering, hybrid approach, and unsupervised and association rule mining algorithms for a movie recommendation, along with various similarity measures. These strategies enable recommendation systems to provide personalized recommendations to users and improve their experience.