A new temporal and social PMF-based method to predict users' interests in micro-blogging
A new temporal and social PMF-based method to predict users' interests in micro-blogging
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DOI：
10.1016/j.dss.2013.02.007
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发表时间：
2013-06-01
影响因子：
7.5
通讯作者：
Gao, Heng
中科院分区：
文献类型：
--
作者：
Bao, Hongyun;Li, Qiudan;Gao, Heng
微博正在成为一个越来越受欢迎的社交媒体平台，用户可以在这里发现关于现实世界的有趣信息，特别是企业能够了解客户的需求。信息的快速传播和微博的便利导致了大量的受众分享他们的日常活动，交换意见，并与他人建立友谊。通过分析用户生成的内容，可以挖掘用户的潜在兴趣，从而帮助微博为用户提供更好的个性化信息服务。随着时间的推移，用户的行为会受到朋友意见和兴趣变化的影响。基于这些直觉，本文提出了一种时间和社会概率矩阵分解模型来预测用户对微博的潜在兴趣。通过利用矩阵分解技术学习用户和话题的潜在特征，该模型分析了时间信息和用户活动(包括发布推文和与他人建立友谊)对用户潜在特征空间和他们感兴趣的话题的影响。该模型提供了一种融合时间信息和社会网络结构的统一方法，以准确预测用户的未来兴趣。在中国最受欢迎的微博网站新浪微博上的实验结果证明了该模型的有效性。(C)2013爱思唯尔B.V.保留所有权利。
Micro-blogging is becoming an increasingly popular social media platform where users can discover interesting information about the real world and especially corporations are able to understand customers' demands. The fast diffusion of information and the convenience of micro-blogging have resulted in large audiences sharing their daily activities, exchanging opinions and establishing friendships with others. By analyzing the user-generated contents, one can explore users' potential interests, which helps micro-blogging provide users with better personalized information services. Users' behaviors are affected by opinions of their friends and changes in their interests over time. Based on these intuitions, in this paper we propose a temporal and social probabilistic matrix factorization model to predict users' potential interests in micro-blogging. By exploiting the matrix factorization technique to learn latent features of users and topics, our model analyzes the impacts of time information and users' activities, including posting of tweets and establishing friendships with others, on the latent feature space of users and topics of their interests. The proposed model provides a unified way to fuse the time information and the social network structure to predict users' future interests accurately. The experimental results on Sina-weibo, one of the most popular micro-blogging sites in China, demonstrate the efficiency and effectiveness of our proposed model. (C) 2013 Elsevier B.V. All rights reserved.