A Graph-Based Content Similarity Approach for User Recommendation in Telegram

Telegram is a cloud-based instant messenger with more than ۵۰۰ million monthly active users. This messenger is very popular among Iranians, as more than ۵۰ million Telegram users are Iranians. Telegram is used as a social network in Iran because it offers features beyond a simple messenger, but does not offer all the features of social networks, including user recommendation. In this paper, investigating a real dataset crawled from Telegram, we have provided a hybrid method using the user membership graph and group characteristics to recommend the user in Telegram. The membership graph connects users based on membership in the same groups. Also, the characteristics for each group are indicated by the name and description of that group in Telegram. We created a bag of words for each group using natural language processing methods, then combined the bag of words for each group with the results of the membership graph processing. Finally, users are recommended based on the list of groups obtained by the combination. The data used in this paper include more than ۹۰۰,۰۰۰ groups and ۱۲۰ million users. Evaluation of the proposed method separately on two categories of Telegram specialized groups shows the model integration and error reduction for the first category to ۰.۰۰۹ and the second category to ۰.۰۱۶ in RMSE.

Keywords:

Recommender systems , Telegram , Social networks , Membership graph , group's characteristics