Research On Recommendation System Based On Similar Cloud And Automatic Updated Algorithm

Recently, with the popularization of Internet and the rapid development of E-commerce, many famous E-Commerce sites have developed Recommender system for providing personalization service to consumers. Recommender systems are used by E-commerce sites to suggest products their customers and to provide consumers with information to help them decide which products to purchase. Recommendation systems, especially collaborative filtering recommendation systems, have achieved widespread successes on the Web. However, the tremendous growth in the amount and variety of available information poses some austere challenges to recommendation systems, the problems of veracity, cold-start and scalability in collaborative filtering recommendation are in dire need to be solved. To address these issues, this paper focuses on collaborative filtering algorithms recommend studied.In this paper, these classic problems in collaborative filtering recommendation algorithm have been made in-depth analysis and exploration. The measurement method of cloud similarity is proposed to solve the sparse problem; Item similarity-based time-window weight is also used in the improved algorithm to reflect the change of users'interest. In order to solve the cold-start problem, this paper adopts a collaborative filtering recommendation algorithm based on the item classification to pre-produce the ratings. Finally, in order to verify the validity of the algorithms, used data sets to do the simulation experiment, the results show that, the improved algorithm both in the recommendation accuracy and response speed was the best, it can improve the collaborative filtering algorithm.Through this research work, sparse and change of interests and cold-start problems in collaborative filtering algorithm have been solved to a certain extent. Meanwhile, the quality and function of electronic commerce recommendation system has been enforced.