Research On Personalized Recommendation System Based Clustering And Immune 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.Firstly, the papers introduced the basic theory of E-Commerce Recommender system, including the conception, effect, classification, inputting module, outputting module, some widely used recommending technologies. Furthermore, the collaborative filtering algorithm was analyzed, including the algorithmic process, existent problems and existing solutions.Secondly, to solve the veracity problem of collaborative filtering algorithm, the papers used the immune system in the operation of nearest neighbor select, considering of the comparability between the artificial immune system shape-space model, and proposed a unique immune recommender algorithm, but it had some defects on the response speed and new user problem, so, through twice improvements, finally get the algorithm which called based on demographic information and clustering immune system recommender algorithm (BDICIR).Finally, in order to verify the validity of the algorithms, used two different data sets to do the simulation experiment, the results show that, BDICIR algorithm both in the recommendation accuracy and response speed was the best, it can improve the collaborative filtering algorithm.