基于深度神经网络链接预测框架的社交网络好友推荐系统

Shivangi Singh, Reshma Rajan, S. Nandini, D. Ramesh, C. Prathibhamol
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引用次数: 2

摘要

在过去的十年里,社交网站已经成为最常见的在线联系方式,这导致了社交网络中潜在的朋友推荐结构的兴起,该结构向用户推荐朋友。不幸的是,大多数现有的朋友推荐框架在向另一个人推荐朋友时,仅仅考虑了共同朋友的数量、地理位置、共同兴趣和其他因素。同时,最近的一些研究已经证明了深度学习和神经网络在推荐系统领域的价值,以及最近在推荐领域使用各种深度学习变体的改进。因此,本文讨论了一种基于混合模型的个性化朋友推荐系统,该模型将链接预测(这是大多数社交媒体平台中广泛使用的传统方法,并遵循friend-of-friend方法)与神经网络模型相结合,以提高准确性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Friend Recommendation System in a Social Network based on Link Prediction Framework using Deep Neural Network
Over the last decade, social networking sites have become the most frequent way to connect online, which has led to the rise of underlying friend recommendation structure in social networks which suggests friends to users. Most existing friend recommendation frameworks, unfortunately, merely take into account the number of mutual friends, geo-location, mutual interests and other factors when recommending one person as a friend to another. Meanwhile, a number of recent research have demonstrated the value of deep learning and neural networks in the areas of recommendation systems, as well as recent improvements in the field of recommendation employing various deep learning variations. Thus, in this paper, a personalized friend recommendation system based on a hybrid model that combines link prediction (which is a widely used traditional method in most social media platforms and follows the friend-of-friend approach) with a neural network model for added accuracy and efficiency, is discussed.
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