Collocating Recommendation Method for E-Commerce Based on Fuzzy C-Means Clustering Algorithm

In order to reduce the time for customers to select commodities they are interested in, improve the purchase efficiency, improve the success rate of sales of merchants, and create greater economic benefits for enterprises and merchants, this project collects information and data of e-commerce users, using neural network model to analyze and mine data characteristics and shopping records of e-commerce users. According to the analysis results, a user commodity recommendation system based on e-commerce is implemented by using data mining technology. Through the combination of database technology, the transaction and browsing data generated in the process of e-commerce transactions are collected. The collected data is preformatted and used as the input of data mining. Then, it uses data mining technology to mine and analyze the commodities that users are interested in, makes matching according to the types of commodities, and recommends the commodities that users are interested in under a given scene according to the established prediction model. By combining fuzzy clustering with collaborative filtering algorithm, this paper recommends the products that users are interested in, which are mined from historical data and commodity information.