论文标题
基于对Telegram社交网络中人格特质的分析的推荐系统
A Recommender System based on the analysis of personality traits in Telegram social network
论文作者
论文摘要
Accessing people's personality traits has always been a challenging task. On the other hand, acquiring personality traits based on behavioral data is one of the growing interest of human beings.许多研究表明，人们在社交网络上花费了大量时间，并显示出在网络空间中创造某些人格模式的行为。 One of these social networks that have been widely welcomed in some countries, including Iran, is Telegram. The basis of this research is automatically identifying users' personalities based on their behavior on Telegram.为此，提取了电报组用户的消息，然后根据NEO个性库存确定每个成员的个性特征。 For personality analysis, the study is employed three approaches, including; Cosine Similarity, Bayes, and MLP algorithms.最后，这项研究提供了一种推荐系统，该系统使用余弦相似性算法探索并根据提取的个性向会员推荐相关的电报频道。 The results show a 65.42% satisfaction rate for the recommender system based on the proposed personality analysis.
Accessing people's personality traits has always been a challenging task. On the other hand, acquiring personality traits based on behavioral data is one of the growing interest of human beings. Numerous researches showed that people spend a large amount of time on social networks and show behaviors that create some personality patterns in cyberspace. One of these social networks that have been widely welcomed in some countries, including Iran, is Telegram. The basis of this research is automatically identifying users' personalities based on their behavior on Telegram. For this purpose, messages from Telegram group users are extracted, and then the personality traits of each member according to the NEO Personality Inventory are identified. For personality analysis, the study is employed three approaches, including; Cosine Similarity, Bayes, and MLP algorithms. Finally, this study provides a recommender system that uses the Cosine similarity algorithm to explore and recommend relevant Telegram channels to members according to the extracted personalities. The results show a 65.42% satisfaction rate for the recommender system based on the proposed personality analysis.
