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There are numerous number of movies available over the world, all of those are not interesting and also impossible to watch for one user. That’s why, a recommendation system is very important for user to find out the suitable product quickly. On the other hand, a recommendation system gives the flexibility of efficient searching rather than manually. In this way, recommendation system plays a prominent role to user. In this study, we have developed a scheme for a movie recommendation system named neural engine-based recommendation system (NERS) for users. In our recommended approach (NERS), we have incorporated data contents about user’s interests via standard movie dataset, that helps us to make a neural engine called neural recommender (NR). We have used two sorts of data sets to make NR, one is general dataset associated with five different nature of data variables, and another one was …