Electronic commerce software agents: the featured-based filtering approach

Software agents can change the nature of interactions on the Internet: from simple access to large databases, to dynamic and personalized information and advice sources. 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 team 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.