An In-Depth Comparative Framework for Movie Recommendation Approaches Across Diverse Algorithms.

In today's digital entertainment environment, movie recommendation algorithms are essential for intensifying user ecstasy and content engagement. Online streaming services like Netflix can potentially increase revenue by implementing a recommendation system that offers tailored movie suggestions to users based on their past interactions with the platform. The strategies we'll discuss here are not simply limited to films; they may be used for any product for which you want to develop a Recommendation System (RS). This paper gives a preamble of several types of recommendation strategies based on user preferences, ratings, domain knowledge, users’ demographic data, user’s context. This work also envisions the singular value decomposition plus-plus (SVD++) and collaborative filtering-based movie recommendation method. The suggested method is compared to well-known machine learning methods such as k closest neighbour (K-NN), singular value decomposition (SVD), and co-clustering. The suggested method is experimentally tested using MovieLens 100 K datasets, and the error of the RS is assessed using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). In order to address issues like scalability, data sparsity, and the cold start problem, our study is driven by the requirement to determine the most efficient method for offering individualized movie suggestions. This technique is used by numerous e-commerce companies, including Amazon, Flipkart, and Mantra, to understand their customers' purchasing patterns and provide recommendations to them about the goods they are most likely to purchase. [ABSTRACT FROM AUTHOR]