Web Log Analysis Using Support Vector Regression

- Journal title : Communications for Statistical Applications and Methods
- Volume 10, Issue 1, 2003, pp.61-77
- Publisher : The Korean Statistical Society
- DOI : 10.5351/CKSS.2003.10.1.061

Title & Authors

Web Log Analysis Using Support Vector Regression

Jun, Sung-Hae; Lim, Min-Taik; Jorn, Hong-Seok; Hwang, Jin-Soo; Park, Seong-Yong; Kim, Jee-Yun; Oh, Kyung-Whan;

Jun, Sung-Hae; Lim, Min-Taik; Jorn, Hong-Seok; Hwang, Jin-Soo; Park, Seong-Yong; Kim, Jee-Yun; Oh, Kyung-Whan;

Abstract

Due to the wide expansion of the internet, people can freely get information what they want with lesser efforts. However without adequate forms or rules to follow, it is getting more and more difficult to get necessary information. Because of seemingly chaotic status of the current web environment, it is sometimes called "Dizzy web" The user should wander from page to page to get necessary information. Therefore we need to construct system which properly recommends appropriate information for general user. The representative research field for this system is called Recommendation System(RS), The collaborative recommendation system is one of the RS. It was known to perform better than the other systems. When we perform the web user modeling or other web-mining tasks, the continuous feedback data is very important and frequently used. In this paper, we propose a collaborative recommendation system which can deal with the continuous feedback data and tried to construct the web page prediction system. We use a sojourn time of a user as continuous feedback data and combine the traditional model-based algorithm framework with the Support Vector Regression technique. In our experiments, we show the accuracy of our system and the computing time of page prediction compared with Pearson's correlation algorithm.algorithm.

Keywords

SVR;Web Log Data;Collaborative Recommendation System;

Language

Korean

Cited by

References

1.

Proceedings of the Workshop on Recommendation system, 1988.

2.

KDDM01, 2001.

4.

SIGIR 2000, 2000.

5.

Data Mining: Concepts and Techniques, 2001.
pp.435-436

7.

Journal of the ACM, 1997.

8.

Proc. 2nd Berkeley symposium on Mathematiccal Statistics and Probabilistics, 1951.
pp.481-492

9.

Proceedings of Speech and Natural Language Workshop, 1991.

10.

Modern Information Retrieval, 1999.
pp.6-8

11.

Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, 1994.

12.

Introduction to Modern Information Retrieval, 1983.

13.

Technical Report, 1996.

14.

Proceedings of the CHI-95 Conference, 1995.

15.

Machine Learning, 1995.
vol.20.
pp.273-297

16.

Statistical Learning Theory, 1998.
pp.445-448

17.

Nature지, 1999.
400,
pp.107-109

18.

2002.

19.

2002.

20.

2002.

21.

2002.

22.

2002.

23.

2002.