In this liveProject, you’ll build a movie recommendation system based on movies that your users have previously interacted with. This system is designed to boost engagement and keep your users on your site. You’ll develop a competitive array of similarity functions, and create your own recommender system based on these similarities. Finally, you’ll evaluate your system’s effectiveness and tune its parameters.

In this liveProject, you’ll build a movie recommendation system based on the content and metadata of movies in your system. This system is intended to maximize the satisfaction of your movie-watching users. You’ll start with an analysis to determine the content of your movies, then use that data to implement content-based similarities for both products and users. You’ll build and evaluate your recommender system based on these connections, till it’s the best it can be!

In this liveProject, you’ll build a movie recommendation system that reduces the dimensionality of the spaces involved to enhance its performance so that latent representations are identified, which aim to capture users and items interactions with the system on the same latent vector space. You’ll work to compute clusters on the movies and clients of your site, and utilize latent representations of users and items with a reduced dimensionality to compute similarities. Once you’ve established this data, you’ll build and test your recommendation engine.

In this liveProject, you’ll determine what similarities there are between certain movies, and then build a recommendation system based on the similarities you’ve identified. Although you’ll be working with movie data for a streaming website, identifying similarities can help enhance recommendation engines for any item or product. You’ll develop a general technique for spotting similarities, then apply your learned similarities to build a recommendation engine. You’ll then enrich your model with collaborative and content-based data, and evaluate and compare your model.