Hybrid recommendation system for movies using artificial neural network

Abstract

RS are becoming more crucial in everyone’s life as the need for recommendations that reflect users’ interests grows every day. For new users, the movie RS based on a film’s ratings has become an exciting trait. However, scalability, cold start, and sparsity issues are the barriers for RS that must be overcome by proposed hybrid system. The MF Technique was utilizedin the modified SVD to minimizethe numberof features & spatial dimensions in the set of data. To detect the similarity between the movies the Content Driven KNN is proposed based on cosine similarity. To measure the closest neighbor, IKSOM is proposed by using EISEN cosine correlation distance which reduce the cluster overlapping. The K means ++ Clustering is proposed for categorize the data in the movies by silhouette method which calculate the optimal quantity of clusters. The overall hybrid method generates the scores also SVD-CF predicts movie ratings and provides the top recommendation for the movies. The results reveal the proposed hybrid system attains high accuracy, precision, Recall and F1-score also achieves the less error as RMSE and MAE when compared to the existing system.