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An online blog reading system by topic clustering and personalized ranking
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ACM Transactions on Internet Technology (TOIT) archive
Volume 9 ,  Issue 3  (July 2009) table of contents
Article No. 9  
Year of Publication: 2009
ISSN:1533-5399
Authors
Xin Li  Peking University
Jun Yan  Microsoft Research Asia
Weiguo Fan  Virginia Tech
Ning Liu  Microsoft Research Asia
Shuicheng Yan  National University of Singapore
Zheng Chen  Microsoft Research Asia
Publisher
ACM  New York, NY, USA
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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.


REFERENCES

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Collaborative Colleagues:
Xin Li: colleagues
Jun Yan: colleagues
Weiguo Fan: colleagues
Ning Liu: colleagues
Shuicheng Yan: colleagues
Zheng Chen: colleagues