Personalized meta-search engine design and implementation
Jiandong Cao, Yang Tang, Binbin Lou · 2010
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.