基于社交网络的用户兴趣建模混合方法

Marina Shafik, R. Elgohary, I. Moawad, Mohamed Roushdy
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引用次数: 1

摘要

社交网络和微博服务的日益普及,逐渐增加了对个性化应用的需求。像Twitter这样的微博服务为用户提供了一个强大的论坛来分享他们的个人兴趣和观点。挖掘和分析用户的兴趣是购买决策和跟踪公众对其商品、业务等的情绪的关键因素。尽管Twitter的实时主题范围很广,但由于非结构化的短文本,它带来了重大挑战。在这篇论文中,通过基于个人用户的推文建立个人用户的简介来研究寻找用户感兴趣的话题的最佳模型。提出了一种基于主题的混合模型,将两种无监督学习算法结合情感考虑和用户特征。因此,我们证明了所提出的混合模型在社交网络用户兴趣的主题提取方面具有更高的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Method for Modeling User Interests based on Social Network
The growing popularity of social networks and microblogging services has gradually increased the demand for personalized applications. The microblogging services such as Twitter has a powerful forum for users to share their personal interest and opinions. Mining and analyzing user's interests is a crucial factor in buying decisions and tracking the emotions of the public about their items, business, etc. Although, Twitter has a broad range of topics in real-time, it poses significant challenges because of the unstructured short text. In this paper, the best model for finding the user's topics of interest is being investigated by building the profile of individual users based on their tweets. A hybrid Topic-based model is proposed that combines both two unsupervised learning algorithms with sentiment consideration and user features. Thus, we show that the proposed hybrid model has a higher performance in the topic extraction of user's interests on Social Networks.
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