Electronic Commerce Software Agents: The Featured-Based Filtering Approach
eCommerce; Neural networks; Software agents; Support vector machines
Software agents can change the nature of interactions on the Internet: from simple access to large databases, to dynamic and personalized information and advice source. This approach becomes more important when product features and attributes are complex and qualitative as well as when the opportunities for differentiation, customization, and tailoring to individual preferences increase. In order to implement a software agent approach as an intelligent recommendation system, these agents have to be intelligent enough to learn their users' criteria and learn how to aggregate information from different mediums and how to help reinforce this information using these mediums. In this paper, we describe several algorithms, which can be appropriate to be the center of such scheme, including supervised learning neural networks and support vector machines.
Robotics, Automation, Control and Manufacturing: Trends, Principles and Applications - Proceedings of the 5th Biannual World Automation Congress, WAC 2002, ISORA 2002, ISIAC 2002 and ISOMA 2002
Number of Pages
Article; Proceedings Paper
Source API URL
Rabelo, Luis C., "Electronic Commerce Software Agents: The Featured-Based Filtering Approach" (2002). Scopus Export 2000s. 2267.