Personalized Movie Recommendations: A Comprehensive Review and Analysis

A significant portion of our life is now occupied by internet technology. Users frequently struggle with the issue of too much information being offered. Recommendation systems (RS) are used to assist users in navigating the explosion of information. RS is mostly employed in e-commerce sites like Amazon, Flipkart, and eBay as well as digital entertainment platforms like Netflix, Prime Video, and IMDB. The two well-established strategies, collaborative filtering and content-based approaches, both possess certain shortcomings. Despite this, any hybrid system that combines the benefits of each approach would result in outcomes that are superior. Scalability, the cold start problem, data sparsity, realistic usage feedback, and verification based on actual implementation are a few key problems that movie recommendation systems currently struggle with issues of preciseness and temporal complexities, that might disqualify recommendation system from use in real-life recommendation systems, are additional challenges that need serious study attention. This review of the literature seeks to compile and organize all the significant flaws found in the most prevalent and widely used commercial movie recommendation systems.