Articles

Most people can't subscribe to every direct-to-consumer platform today, and the number is growing. The platform's content and the user's experience influence the decision to subscribe or buy. Today's consumers anticipate instantaneously curated content exploration, acquisition, and consumption. Media firms actively seek to increase both click-through rate and profitability by enhancing the user experience and enticing customers to subscribe or buy premium content through recommender systems. The direct-to-consumer platforms may maintain user engagement after consumers have visited the contents by providing suggestions that make the most of the site's rich content catalogs. By bringing it to the attention of viewers based on their viewing habits, for instance, effective recommendation systems might boost earnings for underappreciated "long tail" content. This research explores various recommender system types currently in widespread usage with an analysis of some of the fascinating breakthroughs.

The task of recommending products to customers based on their interests is important in business. It is possible to accomplish this with machine learning. To reduce human effort by proposing movies based on the user's interests efficiently and effectively without wasting much time in pointless browsing, the movie recommendation system is designed to assist movie aficionados. This work focuses on developing a movie recommender system using a model that incorporates both cosine similarity and sentiment analysis. Cosine similarity is a standard used to determine how similar two items are to one another. An examination of the emotions expressed in a movie review can determine how excellent or negative a review is and, consequently the overall rating for a film. As a result, determining whether a review is favorable or adverse may be automated because the machine learns by training and evaluating the data. Comparing different systems based on content-based approaches will produce results that are increasingly explicit as time passes.