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

摘要

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 prod-ucts that really to meet their needs. To reduce the product overload of Internet shoppers, a variety of rec-ommendation techniques that track previous actions of groups of consumers to make personalized recommendations have been developed and applied. Current personalized recommendation systems suf-fer 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 ori-ent Web-visitors on how to become consumers, a salesman-like recommendation technology was devel-oped 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 vis-itor favor products can reach 60.8%, showing that the proposed model can achieve recommendation effectiveness.