Blog Recommendation based on Blog Set similartiy and Mergence

Blog provides a simple way for people to share personal experiences and ideas, and has already become an important tool for people to communicate with each other. This fact turns blog into one of the most important information providers of the World Wide Web, but also makes how to find useful blogs for people to read an urgent problem. In this paper, we propose a blog clustering algorithm BCBSM (Blog Clustering based on Blog Set similarity and Mergence) aims at providing a general purpose blog friend recommendation system with high efficiency and effectiveness. By applying BCBSM, blogs are clustered into blog sets and to help improving the effectiveness and efficiency of friend recommendation. We evaluate our method by compare it with traditional blog similarity based recommendation method and measure the result with an automatic measurement. Result shows that our method could help provide better and more reasonable results.