Another look at automatic text-retrieval systems
Communications of the ACM
Automatic text processing
TileBars: visualization of term distribution information in full text information access
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation of a tool for visualization of information retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
PHOAKS: a system for sharing recommendations
Communications of the ACM
Siteseer: personalized navigation for the Web
Communications of the ACM
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Clustering Algorithms
Modern Information Retrieval
A Fuzzy Rule-Based Agent for Web Retrieval-Filtering
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Personalized Product Recommendation in e-Commerce
EEE '04 Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'04)
Using Element and Document Profile for Information Clustering
EEE '04 Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'04)
Hi-index | 0.00 |
In this paper we propose an intelligent web search method with customized results. This approach adopts a cosine method to calculate the similarity between document profile and customer profile. The document profile is derived from the similarity score of documents. The customers' search history is captured to generate customer profile. Then the customized search results are recommended to the end users based upon the similarity between document profile and customer profile.