An E-commerce Personalized Recommendation Algorithm based on Fuzzy Clustering

Abstract

The key to the performance of recommendation system lies in which recommendation technology to choose. Personalized recommendation technology has attracted the extensive attention of more and more merchants and researchers with its humanization and potential commercial value, and earliest and most widely used personalized recommendation technology. However, with the increasing number of users and commodities, the recommendation system is facing some problems, such as the difficulty of ensuring the real-time performance and the decline of recommendation quality. Many scholars put forward many solutions to the shortcomings of collaborative filtering. So as to obtain better recommendation effect. Based on different angles, this paper adopts fuzzy clustering recommendation algorithm. Using fuzzy clustering algorithm to increase population diversity can avoid the lack of diversity of adjacent users; The feature of flexible partition of fuzzy c-means clustering algorithm is used to reduce the dimension of data, so as to effectively solve the problem of recommendation quality degradation caused by data sparsity.