Title

Representation Entry Selection for Profiling Blogs

Publication Type

Conference Proceeding Article

Publication Date

10-2008

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.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

Proceedings of the ACM 17th Conference on Information and Knowledge Management: CIKM'08: Napa Valley, CA, October 26-30, 2008

First Page

1387

Last Page

1388

ISBN

9781595939913

Identifier

10.1145/1458082.1458293

Publisher

ACM

City or Country

New York

Additional URL

http://dx.doi.org/10.1145/1458082.1458293