An Agent-Based Personalized Recommendation System

Personalized recommendation systems can be a good solution for the internet information overload issues.In traditional recommendation systems,more dispersed business and privacy leakage issue can not be solved.Propose an agent-based E-commerce smart recommendation system and utilize content filtering techniques to make recommendation.Based on the privacy of users,use vector space model to mine user preferences and the characteristics evaluation for commodity,introduce the time forgotten function to deal with interest change,generate recommendation sequences based on the collection of information,present the solution for some important and difficult problems.The experiment on data set got from Movielens shows that proposed method can improve the accuracy of the predication and the performance of computing.