Application of salesman-like recommendation system in 3G mobile phone online shopping decision support

Abstract_The rapid growth of e-commerce has confronted
both enterprises and consumers with a new situation.
Whereas companies are finding it harder to survive,
consumers are unable to effectively select the products
that really to meet their needs. To reduce the product
overload of Internet shoppers, a variety of recommendation
techniques that track previous actions of groups of consumers
to make personalized recommendations have been developed
and applied. Current personalized recommendation systems suffer
from the need to analyze large sets of consumer data, or data for
numerous consumers. However, even within a single group, consumer
preferences may differ, and individual preferences may also change with
circumstances. Additionally, the consumer product knowledge influences
their browsing actions. To orient Web-visitors on how to become consumers,
a salesman-like recommendation technology was developed based on visitor
product preference index, which comprises of their product knowledge and
browsing actions in scene. A prototype system for use with high-technology
product, 3G phones, was developed to test the effectiveness of the recommendation
technology. Through a test of 250 objectives, the results show that the recommendation
deviation level can be reduced to 0.49, and exact fit with visitor favor products can reach
60.8%, showing that the proposed model can achieve recommendation effectiveness.