Mohammad Hossein Jafari - Department of Software Engineering Mashhad Branch, Islamic Azad University Mashhad, Iran
Ghamarnaz Tadayou Tabrizi - Department of Software Engineering Mashhad Branch, Islamic Azad University Mashhad, Iran
Mehrdad jalali - Department of Software Engineering Mashhad Branch, Islamic Azad University Mashhad, Iran

Recommender systems detect users' favorites basedon their past behavior and provide them with propersuggestions; however, these systems would encounter problemswhile dealing with users with low or empty usage data. Thisissue leads to the most prominent challenge of such systemscalled cold start. In this paper, we propose a system based onwhich a modified discrete imperialist competitive algorithmwhere tags are clustered using K-medoids algorithm. When anew user logs in and enters his/her tags then the system willsuggest just a few sources with the largest weight. Experimentalresults demonstrate improvement of evaluation criteria forrecommender system in comparison with other methods.