Representative entry selection for profiling blogs

ABSTRACT

Many applications on blog search and mining often meet the challenge of handling huge volume of blog data, in which one single blog could contain hundreds or even thousands of entries. We investigate novel techniques for profiling blogs by selecting a subset of representative entries for each blog. We propose two principles for guiding the entry selection task: representativeness and diversity. Further, we formulate the entry selection task into a combinatorial optimization problem and propose a greedy yet effective algorithm for finding a good approximate solution by exploiting the theory of submodular functions. We suggest blog classification for judging the performance of the proposed entry selection techniques and evaluate their performance on a real blog dataset, in which encouraging results were obtained.

REFERENCES

• 1: Xiubo Geng , Tie-Yan Liu , Tao Qin , Hang Li, Feature selection for ranking, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277811]
• 2: G. Nemhauser, L. Wolsey, and M. Fisher. An analysis of the approximations for maximizing submodular set functions. Mathematical Programming, pages 265--294, 1978.
• 3: Dong Xin , Hong Cheng , Xifeng Yan , Jiawei Han, Extracting redundancy-aware top-k patterns, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA [doi>10.1145/1150402.1150452]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Abstracting methods

General Terms:
Algorithms, Experimentation

Keywords:
blog classification, blog profiling, entry selection