The Simple Recommender offers generalized recommendations to every user based on movie popularity and (sometimes) genre. The basic idea behind this recommender is that movies that are more popular and more critically acclaimed will have a higher probability of being liked by the average audience. This model does not give personalized recommendations based on the user.

The implementation of this model is extremely trivial. All we have to do is sort our movies based on ratings and popularity and display the top movies of our list. As an added step, we can pass in a genre argument to get the top movies of a particular genre.

A recommendation system provides suggestions to the users through a filtering process that is based on user preferences and browsing history. The information about the user is taken as input. The information is taken from the input that is in the form of browsing data. This information reflects the prior usage of the product as well as the assigned ratings. A recommendation system is a platform that provides its users with various contents based on their preferences and likings. A recommendation system takes the information about the user as input. The recommendation system is an implementation of the machine learning algorithms.

Probyto AI allows organizations to build and manage such usecases and track the benefits of AI adoption in their businesses. Currently, FREE 60 minutes AI Consultation is being provided to the registered users in the Probyto AI Demo platform. If you haven’t registered yet, register now to Probyto AI Demo.

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