Ranked Content Advertising in Online Social Networks

Online social networks (OSNs) such as Twitter, Digg and Facebook have become popular. Users post news, photos and videos, etc. and followers of such users then view and comment the posted information. In general, we call the users who produce the information as the information producers, and the users who view the information as the information consumers. The recently popular targeted information advertising systems enable the producers to target users (i.e., consumers). A key problem of the dvertising system is to efficiently find the top-k most desirable targeted users, who next will view the advertised information and perform potential e-commerce activities. Unfortunately, state-of-the-art solutions to find the top-k desirable targeted users in large OSNs incur high space cost and slow running time. In this paper, we focus on designing efficient algorithms to overcome such efficiency issues. Experimental results, over synthetic and real data sets, demonstrate the effectiveness and efficiency of our algorithms.