Providing a recommendation system for recommending articles to users using data mining methods

Abstract

Due to the growing number of articles and books available on the web, it seems necessary to have a system that can extract users' articles and books from the vast 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 article recommendation to the user. In this research, DBSCAN clustering algorithm is used for data clustering. Then we will optimize our data using the firefly algorithm, then the genetic algorithm is used to predict the data, and finally the recommender system based on participatory filtering provides a list of different articles that can be of interest to the user. Be him. The results of the evaluation of the proposed method indicate that this recommending system has a score of 94% in the accuracy of the system. And in the call section, it obtained a score of 91%, which according to the obtained statistics, it can be said that this system can correctly suggest up to 90% of the user's favorite articles to the user.