User Preferences Madm Personalized Recommendation

The rapid development of information technology in the Internet, make the rapid growth of network information resources. Information overload and information lost have severely restricted the use of network information for people. Personalized recommendation services can provide service which can refer to the necessary goods for users, and allow users to easily find the merchandise quickly and smoothly to purchase the goods; and to provide customer information for businesses to grasp the situation of the user, and then further promotional activities. Through effective analysis of user preference information,the business can improve the accuracy of the information recommended, thus increasing the user loyalty for the site, therefore, the existing e-commerce personalized recommendations are paid more and more attention.In this paper, we combine the decision making ideas with personalized recommendation services, and apply the MADM method which based on vague language quantifier-guided OWA operator, and use the complete user preference information in explicit and implicit different ways. Then we analysis the user's personalized incomplete preference information, to provide users the decision-making recommendation services which can meet user personal preferences. This study does not need to match with other user'information, we face to the preference of each individual user's own information. By the process of interacting with the user, we result in the properties and weight about goods of incomplete information, and then recommend meet the needs of specific user preferences goods or services, thus to improve customer satisfaction, so that to improve the accuracy of product recommended can help businesses to increase customer loyalty in the increasingly fierce competition for e-commerce businesses.