User recommendation based on Hybrid filtering in Telegram messenger

Abstract

Over the past decade, social networks and messengers have found a special place in the creation and development of businesses. User recommendation is a very important feature in social networks that has attracted the attention of many users to these environments. Using this system in an instant messenger environment is very useful. Telegram is a cloud-based messenger with more than 400 million monthly active users. Telegram is used as a social network in Iran, but does not offer the most widely used features of social networks, such as recommending users. This feature is important for marketers to find target audience. This paper presents a hybrid filtering-based algorithm to recommend Telegram users. This method combines the membership graph of users with the profile of groups. The membership graph, models users based on their membership in groups. Also, the profile of each group includes the name and description of the group. We have created a bag of words for each group based on natural language processing methods to combine it with the membership graph. After combination process, users are recommended based on the list of groups obtained. The data used in this study is the information of more than 120 million users and 900,000 supergroups in Telegram. This data is obtained through Telegram API by Idekav system. The evaluation of the proposed method has been done separately on two categories of specialized supergroups. Each category includes 25 specialized supergroups in Telegram. Selected supergroups for evaluation have between 2,000 and 10,000 members. Experimental results show the integrity of the model and error reduction in RMSE.