ENHANCING MOVIE RECOMMENDATIONS THROUGH ARTIFICIAL INTELLIGENCE AND PREDICTIVE ANALYTICS

Abstract

Recommendation movie systems primarily aim to offer customers useful product suggestions by relying solely on past interactions. Recommender systems stand out as particularly useful in businesses due to their application of machine learning technologies. This form of recommendation filtering is used to attempt to predict a user’s selection. With the help of data, it forecasts, aims, and even identifies what the consumers’ needs are from an ever-growing assortment of options. Multiple markers such as a user’s search history, their age and background, what they have bought previously, and a lot more, can help locate the users. It helps users locate products and services which are unavailable or difficult for them to find. People now find it difficult to locate and sort through their preferred content due to the deluge of information. This issue has been addressed by recommendation systems (RSs) however, conventional Appen recommendation systems, such as content-based and collaborative filtering, have serious issues with data scalability, data scarcity, and the cold-start problem, all of which call for sophisticated solutions. Data sparsity and a failure to consider the variety of recommended outcomes are two issues with traditional recommendation systems. While the second experiment extended predictions to 4800 movies and produced a SVM 96% accuracy as compared to others.