Reza Molaee fard - Department of Computer Engineering, Islamic Azad University, Dezful branch
Payam Yarahmadi - Department of Computer Engineering,Taras Shevchenko National University of Kyiv

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 ۹۴% in the accuracy of the system. And in the call section, it obtained a score of ۹۱%, which according to the obtained statistics, it can be said that this system can correctly suggest up to ۹۰% of the user's favorite articles to the user.

recommender system, DBSCAN algorithm, Firefly Algorithm, Genetic Algorithm