Representation Entry Selection for Profiling Blogs
Conference Proceeding Article
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.
Computer Sciences | Databases and Information Systems
Data Management and Analytics
Proceedings of the ACM 17th Conference on Information and Knowledge Management: CIKM'08: Napa Valley, CA, October 26-30, 2008
City or Country
ZHUANG, Jinfeng; HOI, Steven C. H.; SUN, Aixin; and JIN, Rong.
Representation Entry Selection for Profiling Blogs. (2008). Proceedings of the ACM 17th Conference on Information and Knowledge Management: CIKM'08: Napa Valley, CA, October 26-30, 2008. 1387-1388. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2382