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Accessing people's personality traits has always been a challenging task. Onthe other hand, acquiring personality traits based on behavioral data is one ofthe growing interest of human beings. Numerous researches showed that peoplespend a large amount of time on social networks and show behaviors that createsome personality patterns in cyberspace. One of these social networks that havebeen widely welcomed in some countries, including Iran, is Telegram. The basisof this research is automatically identifying users' personalities based ontheir behavior on Telegram. For this purpose, messages from Telegram groupusers are extracted, and then the personality traits of each member accordingto the NEO Personality Inventory are identified. For personality analysis, thestudy is employed three approaches, including; Cosine Similarity, Bayes, andMLP algorithms. Finally, this study provides a recommender system that uses theCosine similarity algorithm to explore and recommend relevant Telegram channelsto members according to the extracted personalities. The results show a 65.42%satisfaction rate for the recommender system based on the proposed personalityanalysis.

A Recommender System based on the analysis of personality traits in Telegram social network