An Introduction on Separating Gray Sheep Users in Personalized Recommender Systems Using Clustering Solution
Nowadays, regarding the development of the Internet and the uncontrolled increase in the amount of information, users have faced difficulties for retrieving information for webs. To solve retrieving information, Personalized Recommender Systems appeared. These systems provide information appropriate to users' needs regarding information obtained from them and their behaviors in searching texts. But, one of the commonest difficulties in Personalized Recommender Systems is the existence of users called grey sheep users. These users have little similarity with other users; therefore, their presence in these systems along with normal users causes the reduction in the precision of predictions and suggestions for the two groups. An appropriate method for reducing the effect of the presence of grey sheep users is the use of clustering methods. Therefore, in the present study, clustering methods for separating users and reducing the mean absolute error as well as increasing the precision of clustering methods. To separate grey sheep users, some suggestions for future research were presented.