Recommendation systems are the world of artificial intelligence becomes extremely popular and large advantage to streaming services like YouTube, Netflix, Amazon Prime, and plenty of and many. The main purpose is to focus on their content to a particular audience. These recommendation systems were terribly robust in their predictions within which they'll dynamically improve the state of what the user sees on the page that's primarily based on the user’s interface with the app. during this paper; we tend to have proposed a hybrid recommender system by using Deep learning approach. This approach deals with the content based information and collaborative data separately at first, then it combines the applications to come up with a system with the simplest of each data. By using the MovieLens of 20M Dataset, we have developed a movie-to-movie recommender system that recommends the movies like that movie was given as input . To develop the hybrid based model, we tend to aggregate the results associated with an auto encoder that determines the content-based movie impacts from the tag data, and an entity of neural network that determines data embeddings of collaborative-based knowledge from ratings data.
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