Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Intermediaries personalize information streams
Communications of the ACM
Towards adaptive Web sites: conceptual framework and case study
Artificial Intelligence - Special issue on Intelligent internet systems
Personalizing web sites for mobile users
Proceedings of the 10th international conference on World Wide Web
Adaptive Web Sites: Conceptual Cluster Mining
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Adaptive web sites: an AI challenge
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Adaptive web navigation for wireless devices
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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Most e-commerce web sites are very large, often confusing and overwhelm the visitor with a huge amount of information. People cannot easily find what they are looking for. Moreover, the web site is presented in the same format to every visitor, irrespective of his needs. This paper proposes a model to solve the above problems. Our model divides the visitors into groups. Then it arranges web pages in the decreasing order of preference for each group and applies path prediction to find sink pages for that group. The results of these algorithms are displayed in a separate frame without modifying the site. Secondly, our paper addresses the need to guide visitors during the configuration of a product. We propose to apply data mining on the quotes for every group and find associations between different components of the product. We can then display these results as suggestions while the prospective buyer is configuring the product. These suggestions will dynamically change as each selection is made. We have discussed the data mining algorithms applicable to our model.