An online blog reading system by topic clustering and personalized ranking

Abstract

There is an increasing number of people reading, writing, and commenting on blogs. According to a recent survey made by Technorati, there are about 75,000 new blogs and 1.2 million new posts everyday. However, it is difficult and time consuming for a blog reader to find the most interesting posts in the huge and dynamic blog world. In this article, an online Personalized Blog Reader (PBR) system is proposed, which facilitates blog readers in browsing the coolest and newest blog posts of their interests by automatically clustering the most relevant stories. PBR aims to make a user's potential favorite topics always ranked higher than those nonfavorite ones. This is accomplished in the following steps. First, the system collects and provides a unified incremental index of posts coming from different blogs. Then, an incremental clustering algorithm with a flexible half-bounded window of observation is proposed to satisfy the requirements of online processing. It learns people's personalized reading preferences to present a user with a final reading list. The experimental results show that the proposed incremental clustering algorithm is effective and efficient, and the personalization of the PBR performs well.