Abstract
The rapid growth of e-commerce has caused product overload where the customer is no longer able to effectively choose the products he/she is exposed to. To overcome the product overload of Internet shoppers, several recommender systems have been developed. Recommendation systems track past actions of a group of customers to make a recommendation to individual members of the group. We introduce a personalized recommendation procedure by which we can get further recommendation effectiveness when applied to Internet shopping malls. The suggested procedure is based on Web usage mining, product taxonomy, association rule mining, and decision tree induction. We applied the procedure to a leading Internet shopping mall in Korea for performance evaluation, and some experimental results are provided. The experimental results show that choosing the right level of product taxonomy and the right customers increases the quality of recommendations.
| Original language | English |
|---|---|
| Pages (from-to) | 301-313 |
| Number of pages | 13 |
| Journal | Electronic Commerce Research and Applications |
| Volume | 1 |
| Issue number | 3-4 |
| DOIs | |
| Publication status | Published - 2002 Dec 1 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Computer Networks and Communications
- Marketing
- Management of Technology and Innovation
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A personalized recommendation procedure for Internet shopping support. / Kim, Jae Kyeong; Cho, Yoon Ho; Kim, Wooju; Kim, Je Ran; Suh, Ji Hae.
In: Electronic Commerce Research and Applications, Vol. 1, No. 3-4, 01.12.2002, p. 301-313.Research output: Contribution to journal › Article
TY - JOUR
T1 - A personalized recommendation procedure for Internet shopping support
AU - Kim, Jae Kyeong
AU - Cho, Yoon Ho
AU - Kim, Wooju
AU - Kim, Je Ran
AU - Suh, Ji Hae
PY - 2002/12/1
Y1 - 2002/12/1
N2 - The rapid growth of e-commerce has caused product overload where the customer is no longer able to effectively choose the products he/she is exposed to. To overcome the product overload of Internet shoppers, several recommender systems have been developed. Recommendation systems track past actions of a group of customers to make a recommendation to individual members of the group. We introduce a personalized recommendation procedure by which we can get further recommendation effectiveness when applied to Internet shopping malls. The suggested procedure is based on Web usage mining, product taxonomy, association rule mining, and decision tree induction. We applied the procedure to a leading Internet shopping mall in Korea for performance evaluation, and some experimental results are provided. The experimental results show that choosing the right level of product taxonomy and the right customers increases the quality of recommendations.
AB - The rapid growth of e-commerce has caused product overload where the customer is no longer able to effectively choose the products he/she is exposed to. To overcome the product overload of Internet shoppers, several recommender systems have been developed. Recommendation systems track past actions of a group of customers to make a recommendation to individual members of the group. We introduce a personalized recommendation procedure by which we can get further recommendation effectiveness when applied to Internet shopping malls. The suggested procedure is based on Web usage mining, product taxonomy, association rule mining, and decision tree induction. We applied the procedure to a leading Internet shopping mall in Korea for performance evaluation, and some experimental results are provided. The experimental results show that choosing the right level of product taxonomy and the right customers increases the quality of recommendations.
UR - http://www.scopus.com/inward/record.url?scp=0347938279&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0347938279&partnerID=8YFLogxK
U2 - 10.1016/S1567-4223(02)00022-4
DO - 10.1016/S1567-4223(02)00022-4
M3 - Article
AN - SCOPUS:0347938279
VL - 1
SP - 301
EP - 313
JO - Electronic Commerce Research and Applications
JF - Electronic Commerce Research and Applications
SN - 1567-4223
IS - 3-4
ER -