Personalized information filtering based on semantic similarity
Wang Hongsheng, Shu Xiaoming · 2010
In order to meet the retrieval needs of different users and get more accurate retrieval results, a personalized information filtering algorithm based on semantic similarity is proposed. In this paper, semantic web is used to describe the user interest information and document information, thus filtering system can improve the semantic understanding of user interest and retrieval documents, and make personalized information filtering more accurately by calculating the semantic similarity based on semantic web. An experiment is designed to test the precise performance of the algorithm. The testing results show that the accuracy of personalized information filtering is highly improved by using this algorithm.