An Intelligent E-commerce Recommender System Based on Web Mining | Zendy

Ziming Zeng | Zendy

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An Intelligent E-commerce Recommender System Based on Web Mining

Author(s) -

Ziming Zeng

Publication year - 2009

Publication title -

international journal of business and management

Language(s) - English

Resource type - Journals

eISSN - 1833-8119

pISSN - 1833-3850

DOI - 10.5539/ijbm.v4n7p10

Subject(s) - recommender system , computer science , the internet , information overload , web mining , product (mathematics) , e commerce , prosperity , world wide web , quality (philosophy) , customer satisfaction , trace (psycholinguistics) , web page , business , marketing , philosophy , linguistics , geometry , mathematics , epistemology , political science , law

The prosperity of e-commerce has changed the whole outlook of traditional trading behavior. More and more people are willing to conduct Internet shopping. However, the massive product information provided by the Internet Merchants causes the problem of information overload and this will reduces the customer’s satisfaction and interests. To overcome this problem, a recommender system based on web mining is proposed in this paper. The system utilizes web mining techniques to trace the customer’s shopping behavior and learn his/her up-to-date preferences adaptively. The experiments have been conducted to evaluate its recommender quality and the results show that the system can give sensible recommendations, and is able to help customers save enormous time for Internet shopping.

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