Individual Search Agent Based on Machine Learning
Hong‐Ying Chen · Microelectronics & Computer · 2006
This paper uses methods of machine learning to enhance the exactness of search engine. It aims to submit a character string which is similar to users' interesting. Decision-tree is used to learn the users' interesting. There are several problems using decision-tree in web pages: how to deal with when the samples have not enough characters; many characters have different importance, how to reflect it in decision-tree; when and how to recreate the decision-tree; perfect tree problem; when we delete character string from decision-tree, which should be chosen; These situations are investigated and solved. Performance of new decision-tree is: validity has enhanced from 70% to 75.4%.