International Conference on Computer and Knowledge Engineering

Home / 11th International Conference on Computer and Knowledge Engineering

GroupRec: Group Recommendation by Numerical Characteristics of Groups in Telegram

Authors :

Davod Karimpour¹ Mohammad Ali Zare Chahooki² Ali Hashemi³

1- Computer Engineering Department, Yazd University, Yazd, Iran 2- Computer Engineering Department, Yazd University, Yazd, Iran 3- Computer Engineering Department, Yazd University, Yazd, Iran

Keywords :

Recommendation of social groups, Telegram, social networks (online), computational modeling, business.

Abstract :

Today, recommender systems are used in many different businesses to find items of interest to users. The use of these systems is widely found in online economic systems and social networks. Therefore, using these systems in the messaging environment will cause changes and transformations for marketing. Telegram is a cloud-based messenger with more than 500 million monthly active users. This messenger has a relatively acceptable position compared to its other competitors, because the security and features provided in it, have made it different from other messengers and close to social networks. One of the most popular features of messengers is groups. Many marketers are looking for groups that fit their field. One of the main gaps in the messengers regarding advertising and marketing to expand businesses is the impossibility of finding social groups. In this paper, a new method for group recommendation in the Telegram is presented. This method, by receiving a set of users, analyzes their groups and recommends a list of ranked groups. The proposed method is created by combining the previous two methods in the field of group recommendation and computational modeling of numerical variables obtained from each group. This study is dependent on the information of all users due to the use of the membership graph, and the behavior of the system changes by the information extracted from the users. The results of experimental experiments show a significant reduction of RMSE and MAE in the proposed method compared to the previous two methods.