Case-intelligence recommendation system modeling based on RS and RBF

Many web tools have been developed for information retrieval and information filtering to help users search, locate and manage web documents to find their needs, while IEEE Internet Computing points out that current system can not meet the real large-scale ecommerce demands, for the data from real websites is complex and multi-expression. Intelligent recommendation systems have been proposed to trace down to the acquirement of the personalized knowledge, whose system model suggests to acquire the user´s preferences from the former stored cases to satisfy the personalized needs. So we select duplex techniques-rough sets (RS) and radial basis function network (RBF) - to conquer those problems caused by users´ data, which are large in records, but rare in attributes. The subsequent research indicates that the newcomer can avoid sensitive to the noise along with the influence of irrelevant items, with which the results of our experiments for the validation on the proposed model are applausive.