A Novel E-Commerce Recommendation System Model based on the Pattern Recognition and User Behavior Preference Analysis
Ning Wang, Qiaoling Zhang, Liejun Yang, Mingming Chen · Advanced science and technology letters · 2016
This paper proposes the novel E-Commerce recommendation system model based on the pattern recognition and user behavior preference analysis. With the development of Internet and the generation of the huge amounts of data, information overload problem is increasingly serious, user drown in an ocean of data, it is difficult to effectively find themselves interested in the general information. Recommendation system technology was put forward and is widely used in this case, the recommendation system analysis of the user's past behavior records, while using the recommended algorithm automatically recommend users might be interested in the information to the general user. Standard utility of the log file format stored physical information about the client connection, if it can be some of the files stored in the mining analysis as can see the customer's behavior. From this starting point, we propose the new recommendation system architecture with the integration of pattern recognition algorithm that is innovative.