A Novel Personalized Recommendation System of Digital Resources Based on Semantics

Personalized recommendation is hot issue in information management system nowadays. With the technology, it can improve the QOS of information service. In this paper, we present a new user profile model based on semantic meta-model of digital resource, using implicit feedback, the users´ profiles can be adjusted in time. Comparing the ´like´ query in the standard SQL in relational databases, which can not decide the similarity according users´ interests when keywords appear in several different fields, a novel similarity evaluation is given in algorithm of the personalized recommendation. Using the method, a personalized digital resources management system is developed, which can provide high quality information service, and it works well in practice.