Movie recommendation systems have become ubiquitous in most sides of our lives. Currently, they are far fromoptimal. This paper presents a movielense recommendation system based on machine learning through utilizing the deepconvolutional network and depending on generative modeling of public previous aspects mixtures. The objective of thispaper is to introduce such a recommendation system to help users in selecting datasets of movies according to certainpre-specified measurements and data. The applied methodology is pivoted on implementing the system by usingdifferent sentimental analysis algorithms. These algorithms are keen to provide a solution for the full stack developersthrough using a trained model using their datasets. This will give suggestions based on their previous activity orrecommended by other users’ interests demonstrated on their website. Thus to help users visualize their interest or toform the better scope of visualization. The presented system has proved better results concerning accuracy and efficiencyin comparison with some other similar works. When experimentations on both real and synthetic datasets wereconducted, the system showed percentile improvement of about 91.07%in the training dataset and 93.49%in the testingdataset respectively. This system is convenient for several application fields like time series network visualization,business process modeling, various data mining applications, e-commerce websites, besides most online platforms thatpeople use including social media.
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