A Case-Intelligence Recommendation System on Massive Contents Processing through RS and RBF

Abstract

Though many varieties of recommendation systems have been developed to greatly promote the intelligent level of E-commerce websites for recent years, IEEE Internet Computing points out that current system can not meet the real large-scale e-commerce demands", "and has some weakness such as low precision and slow reaction. The personalized recommendation system model based on case intelligence have proposed, which is a comprehensive expression with combination representation of human sense, logics and creativity, and can acquire the user's preferences from the former stored cases to satisfy the personalized needs. The paper focuses on how to perform effective demands on massive contents in websites, so rough sets (RS) and radial basis function network (RBF) techniques are selected to conquer problems caused by the large amounts of data. The new recommender firstly drills from the huge data in RS and reducts the main attributes, and then RBF retrieves the most valuable similar case for recommendation, which processes the same similar knowledge reasoning. The subsequent research indicates that the integrated system gives a fine performance as shown in our experiments.