Abstract
The more pages Web sites consist of, the more difficult the users find it to rapidly reach their own target pages. Ill-structured design of Web sites also prevents the users from rapidly accessing the target pages. In this paper, we describe two complementary approaches to Web usage mining as a key solution to these issues. First, we describe an adaptable recommendation system called the system L-R, which constructs user models by classifying the Web access logs and by extracting access patterns based on the transition probability of page accesses and recommends the relevant pages to the users based both on the user models and the Web contents. We have evaluated the prototype system and have obtained the positive effects. Second, we describe another approach to constructing user models, which clusters Web access logs based on access patterns. We also have found that the user models help to discover unexpected access paths corresponding to ill-formed Web site design.
| Original language | English |
|---|---|
| Pages (from-to) | 253-267 |
| Number of pages | 15 |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 2593 |
| Publication status | Published - 2003 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Theoretical Computer Science
Cite this
On the effectiveness of web usage mining for page recommendation and restructuring. / Ishikawa, Hiroshi; Ohta, Manabu; Yokoyama, Shohei; Nakayama, Junya; Katayama, Kaoru.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2593, 2003, p. 253-267.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - On the effectiveness of web usage mining for page recommendation and restructuring
AU - Ishikawa, Hiroshi
AU - Ohta, Manabu
AU - Yokoyama, Shohei
AU - Nakayama, Junya
AU - Katayama, Kaoru
PY - 2003
Y1 - 2003
N2 - The more pages Web sites consist of, the more difficult the users find it to rapidly reach their own target pages. Ill-structured design of Web sites also prevents the users from rapidly accessing the target pages. In this paper, we describe two complementary approaches to Web usage mining as a key solution to these issues. First, we describe an adaptable recommendation system called the system L-R, which constructs user models by classifying the Web access logs and by extracting access patterns based on the transition probability of page accesses and recommends the relevant pages to the users based both on the user models and the Web contents. We have evaluated the prototype system and have obtained the positive effects. Second, we describe another approach to constructing user models, which clusters Web access logs based on access patterns. We also have found that the user models help to discover unexpected access paths corresponding to ill-formed Web site design.
AB - The more pages Web sites consist of, the more difficult the users find it to rapidly reach their own target pages. Ill-structured design of Web sites also prevents the users from rapidly accessing the target pages. In this paper, we describe two complementary approaches to Web usage mining as a key solution to these issues. First, we describe an adaptable recommendation system called the system L-R, which constructs user models by classifying the Web access logs and by extracting access patterns based on the transition probability of page accesses and recommends the relevant pages to the users based both on the user models and the Web contents. We have evaluated the prototype system and have obtained the positive effects. Second, we describe another approach to constructing user models, which clusters Web access logs based on access patterns. We also have found that the user models help to discover unexpected access paths corresponding to ill-formed Web site design.
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M3 - Article
AN - SCOPUS:35248900417
VL - 2593
SP - 253
EP - 267
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
ER -