Hybrid model for movie recommendation system using content K-nearest neighbors and restricted Boltzmann machine

<span>One of the most commonly used techniques in recommendation framework is collaborative filtering (CF). It performs better with sufficient records user rating but not good sparse data. Content-based works well dataset as it finds similarity between movies by using attributes movies. RBM an energy-based model serving a backbone deep learning and prediction. However, prediction preferable single model. The hybrid achieves results integrating more than one This paper analyses weighted CF system content K-nearest neighbors (KNN) restricted Boltzmann machine (RBM). Movies are recommended to active proposed effects both content-based filtering. Model efficacy was tested MovieLens benchmark datasets.</span>