Personalized meta-search engine design and implementation

Personalized meta-search engine is one search engine that we teach the machine to learn users´ interest, so the search engine can help users to pick up the useful information for them quickly by using their interest keeping in the database. Personalized meta-search engine can sort the results according to users´ interest, the results that user likes will be the top of the results. It is a good measure to use Vector Space Model to help us implement the personalization. We use Vector Space Model to model the user and the results´ interest, then we use cosine angel to calculate the similarity of these interest. This paper describes the design and implementation of this system by using result and user modeling.