Hybridization Unleashed: Advancing Movie Recommendations Through Collaborative, Content Based and Hybrid Filtering

A vast amount of data is available for customers to explore, making it challenging to sift through and find relevant solutions according to their preferences. Recommendation systems offer a way to filter out important information from irrelevant content and provide personalized suggestions to customers. Recently, there's been a growing interest in creating recommendation systems that suggest movies and books based on user preferences due to the surge in digital content. However, existing systems usually focus solely on movies or books, neglecting cross-domain recommendations. To overcome these constraints, the present study introduces a hybrid movie recommendation system. By amalgamating collaborative and content-based filtering, this system strives to provide users with more accurate and varied suggestions. Through experimentation using a dataset comprising movies and books, the findings reveal that the proposed system surpasses traditional collaborative and content-based filtering approaches in both accuracy and diversity. Additionally, the system effectively tackles the cold-start issue associated with a lack of data for new users or items. With its potential to extend beyond movies, this hybrid recommendation system holds the promise of providing personalized and diverse recommendations across various domains.