›› 2012,Vol. 27 ›› Issue (3): 624-634.doi: 10.1007/s11390-012-1249-5

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微博社区中基于拓扑的用户推荐

Marcelo G. Armentano, Daniela Godoy, and Analia Amandi   

  • 收稿日期:2011-09-02 修回日期:2012-01-10 出版日期:2012-05-05 发布日期:2012-05-05

Topology-Based Recommendation of Users in Micro-Blogging Communities

Marcelo G. Armentano, Daniela Godoy, and Analia Amandi   

  1. High Institute of Software Engineering Tandil, National University of the Center of Buenos Aires Province, Tandil Buenos Aires, Argentina National Council of Scientific and Technological Research, Av. Rivadavia 1917, CABA, Argentina
  • Received:2011-09-02 Revised:2012-01-10 Online:2012-05-05 Published:2012-05-05
  • About author:Marcelo G. Armentano re-ceived the Ph.D. degree in computer science from the National Univer-sity of the Center of Buenos Aires Province (UNICEN) in 2008. He is an assistant teacher in the Com-puter Science Department at UNI-CEN, member of High Institute of Software Engineering Tandil (ISIS-TAN) and researcher at National Council of Scientific and Technological Research (CON-ICET). His research interests include personal assistants, recommender systems, user profiling and text mining.
  • Supported by:

    This research was partially supported by the National Scientific and Technical Research Council (CONICET) of Argentina under Grant PIP No. 114-200901-00381.

如今,越来越多的用户通过微博社区,如Twitter、Tumblr或Plurk等,来共享实时新闻和信息.在这类微博网站上,信息通过由关注者和被关注者所构成的社会网络实现共享,关注者可以收到他(她)所关注的人的所有微博.随着这类网站注册用户数的逐步增长,查询相关且可靠的信息成为一个非常重要的问题.由于单条微博字符数的减少,并伴随着非形式化用语的广泛使用,导致标准的基于内容的方法很难用于用户推荐.为了解决这一问题,我们研究了判断用户是否为优质信息源的不同因素,提出了一种利用网络拓扑结构推荐相关用户的算法.在一组微博用户上所进行的实验评估证明了该方法潜在的优异性能.

Abstract: Nowadays, more and more users share real-time news and information in micro-blogging communities such as Twitter, Tumblr or Plurk. In these sites, information is shared via a followers/followees social network structure in which a follower will receive all the micro-blogs from the users he/she follows, named followees. With the increasing number of registered users in this kind of sites, finding relevant and reliable sources of information becomes essential. The reduced number of characters present in micro-posts along with the informal language commonly used in these sites make it difficult to apply standard content-based approaches to the problem of user recommendation. To address this problem, we propose an algorithm for recommending relevant users that explores the topology of the network considering different factors that allow us to identify users that can be considered good information sources. Experimental evaluation conducted with a group of users is reported, demonstrating the potential of the approach.

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