Yu Yan, Qiu Robin G
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ; Computer Science Department, Southeast University Chengxian College, Nanjing 210088, China.
College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ; Information Science Department, Pennsylvania State University, Great Valley, Malvern, PA 16802, USA.
ScientificWorldJournal. 2014;2014:420841. doi: 10.1155/2014/420841. Epub 2014 Mar 31.
Microblog that provides us a new communication and information sharing platform has been growing exponentially since it emerged just a few years ago. To microblog users, recommending followees who can serve as high quality information sources is a competitive service. To address this problem, in this paper we propose a matrix factorization model with structural regularization to improve the accuracy of followee recommendation in microblog. More specifically, we adapt the matrix factorization model in traditional item recommender systems to followee recommendation in microblog and use structural regularization to exploit structure information of social network to constrain matrix factorization model. The experimental analysis on a real-world dataset shows that our proposed model is promising.
微博为我们提供了一个新的交流和信息共享平台，自几年前出现以来一直在呈指数级增长。对于微博用户来说，推荐能够作为高质量信息源的关注对象是一项具有竞争力的服务。为了解决这个问题，在本文中我们提出一种带有结构正则化的矩阵分解模型，以提高微博中关注对象推荐的准确性。更具体地说，我们将传统物品推荐系统中的矩阵分解模型应用于微博中的关注对象推荐，并使用结构正则化来利用社交网络的结构信息约束矩阵分解模型。在一个真实世界数据集上的实验分析表明，我们提出的模型很有前景。
