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

Under the general trend of mobile Internet, more and more industries began to change from traditional business model to e-commerce, which made the scale of e-commerce in China expand rapidly. E-commerce recommendation system provides users with commodity information and suggestions on the basis of understanding and learning customers' needs and preferences, recommends products that may be of interest to users, and helps users complete the purchase process. Collaborative filtering is the most widely used and successful recommendation technology in the recommendation system. However, with the increase of the number of users and commodities in the e-commerce system, the time spent searching the nearest neighbor of the target user in the whole user space also increases sharply, which leads to the decline of system performance. In this paper, a cooperative recommendation implementation method based on user clustering is proposed. Users are clustered based on their scores of commodity categories, and only users' nearest neighbors are searched in the categories to which they belong, so as to search as many nearest neighbors as possible in as little user space as possible.