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

Abstract

Telegram is a cloud-based instant messenger with more than 500 million monthly active users. This messenger is very popular among Iranians, as more than 50 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 900,000 groups and 120 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 0.009 and the second category to 0.016 in RMSE.

Keywords

• recommender systems
• telegram
• social networks
• membership graph
• group's characteristics