Provide a video recommendation system using collaborative filtering and data mining methods

Due to the growing number of videos available on the web, it seems necessary to have a system that can extract users' favorite videos from a huge amount of information that is increasing day by day. One of the best ways to do this is to use referral systems. In this research, a method is provided to improve the recommender systems in the field of film recommendation to the user. In this research, DBSCAN clustering algorithm is used for data clustering. Then we will optimize our data using the cuckoo algorithm, then the genetic algorithm is used to predict the data, and finally, using a recommender system based on participatory refinement, a list of different movies that can be of interest to the user is provided. The results of evaluating the proposed method indicate that this recommender system obtained a score of 99% in the accuracy of the system and a score of 95% in the call section Suggest the user's favorite videos correctly to the user.