Group recommendation in Telegram by membership graph analyzing
Motahareh Mohammad Rezai, Mohammad Ali Zare Chahooki · 2022
Telegram currently has nearly 500 million monthly active users. It is known as a social network in many countries, such as Iran. Due to the diversity and increase of groups in this messenger, it has become difficult to find groups that are related to the users’ interests; so it is better for users to find suitable groups with the help of recommender systems. In this paper, we present a new collaborative filtering method for a group recommender system. It is based on the users’ interests, using and analyzing the graph of users’ membership. The proposed method mainly consists of two parts. In the first step, the main user and top similar users are extracted, then we list the top similar users’ groups. For each similar user, we calculate a score based on the public and the number of user’s groups for any group in the list. We consider the total score of all the top similar users for each group in the list as the group rank. In the second step, due to the existence of groups with only one similar user, the score of popular groups changes according to the number of dissimilar members in each group. Experiments have been performed to determine the effectiveness of these methods on Telegram data. The actual data of this research includes more than 700,000 supergroups and 70 million Telegram users. To evaluate the accuracy and performance of this research precision, recall and f-measure are used and the results confirm the usefulness and effectiveness of the proposed method.