Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10321576 | Electronic Commerce Research and Applications | 2005 | 18 Pages |
Abstract
Product recommendation and preference tracking systems have been adopted extensively in e-commerce businesses. However, the heterogeneity of product attributes results in undesired impediment for an efficient yet personalized e-commerce product brokering. Amid the assortment of product attributes, there are some intrinsic generic attributes having significant relation to a customer's generic preference. This paper proposes a novel approach in the detection of generic product attributes through feature analysis. The objective is to provide an insight to the understanding of customers' generic preference. Furthermore, a genetic algorithm is used to find the suitable feature weight set, hence reducing the rate of misclassification. A prototype has been implemented and the experimental results are promising.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Sheng-Uei Guan, Tai Kheng Chan, Fangming Zhu,