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首页> 外文会议>International Conference on Electrical, Computer and Communication Engineering >Prediction of Preferred Personality for Friend Recommendation in Social Networks using Artificial Neural Network
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Prediction of Preferred Personality for Friend Recommendation in Social Networks using Artificial Neural Network

机译：使用人工神经网络预测社交网络中朋友推荐的首选个性

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摘要

Social Networking sites have nowadays become the most common way to communicate over online for people around the world. For making friends in social network, there remains an underlying friend recommendation framework which suggests friends to the users. However, most of the existing friend recommendation frameworks consider only the number of mutual friends, geo-location, mutual interests etc. to recommend one person as a friend to another. But, in real life, people, who have similar personalities, tend to become friends to each other, application of which is completely missing in the modern friend recommendation frameworks. Hence, we have proposed a personality based friend recommendation framework in this paper, which consists of a 3-Layered Artificial Neural Network for friend preference classification and a distance-based sorted subset selection procedure for friend recommendation. Our model tends to achieve a fairly high precision, recall, fl-measure and accuracy of around 85 %,85%,82% and 83% respectively in the friend choice classification task.
机译：现在，社交网站现在成为世界各地人民在线沟通的最常见方式。为了在社交网络中交朋友，仍然存在潜在的朋友推荐框架，这向用户暗示了朋友。然而，大多数现有的朋友推荐框架只考虑共同朋友，地理位置，共同兴趣等的数量，推荐一个人作为另一个人。但是，在现实生活中，有类似个性的人往往是彼此的朋友，在现代朋友推荐框架中的应用完全缺失。因此，我们在本文中提出了一个基于个性的朋友推荐框架，它包括用于朋友偏好分类的3层人工神经网络和朋友推荐的基于距离的排序子集选择过程。我们的模型倾向于在朋友选择分类任务中实现相当高的精度，回忆，卷曲，效果约为85％，85％，82％和83％。

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