Model and Implementation of E-commerce Recommendation System Based on User Clustering

The growth of data volume provides more opportunities for the development of e-commerce, but it also produces the problem of information overload. That is, users can't quickly and accurately obtain the really interesting demand information from massive commodity information. The existing e-commerce website search systems and search engines mainly filter information through the search conditions input by users, without personalized consideration, and cannot provide convenient, efficient and accurate services according to the needs of users. Collaborative filtering is the most widely used and successful recommendation technology in recommendation system. However, with the increase of the number of users and commodities in e-commerce system, the time consumption of searching the nearest neighbor of the target user in the whole user space also increases dramatically, which leads to the performance degradation of the system. In this paper, a collaborative recommendation method based on user clustering is proposed. Firstly, the user's score on the commodity category is calculated, and then the user is clustered based on the user's score on the commodity category.