An Intelligent E-commerce Recommender System Based on Web Mining

Ziming Zeng

Abstract


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.

Full Text: PDF DOI: 10.5539/ijbm.v4n7p10

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

International Journal of Business and Management   ISSN 1833-3850 (Print)   ISSN 1833-8119 (Online)

Copyright © Canadian Center of Science and Education

To make sure that you can receive messages from us, please add the 'ccsenet.org' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.