Algorithmic Application of Evidence Theory in Recommender Systems

Abstract

With the development of wireless network and various communication technologies, the information on the Internet is expanding rapidly. The development of wireless network and various communication technologies has promoted the development of e-commerce, and people can understand a large amount of commodity information without leaving home. However, due to the complex information on the network, users need to pass a lot of screening to obtain the information they want, and a large amount of irrelevant information will cause users to consume a large amount of irrelevant information. To solve these problems, the personalized recommendation system is created, but the recommendation system is recommended according to the characteristics of users’ interest and shopping behavior. However, users’ interests will change, so they need to use other technologies to screen for relevant commodity information. Evidence theory has a strong ability to distinguish between true and false information and to deal with uncertain information. To this end, this article studies the application of the evidence theory in the recommendation system and finds that the evidence theory algorithm can infer the information needed by users based on the uncertain information. Moreover, the experiment in this article proves that the algorithm application of the evidence theory in the recommendation system can well grasp the interests of users and recommend the information needed by users. This improves the efficiency of users to obtain the required information and achieves 80 points for content recommendations.