Clustering-based personalized shopping guide system

The invention relates to the technical field of electronic commerce, in particular to a shopping guide system for providing personalized recommendation for a target user by utilizing commodity attributes, user historical score data and other information. The system comprises a data collection module, a behavior quantification module, a commodity category screening module, a matrix filling module,a user clustering module and a recommendation generation module. The data collection module is used for collecting commodity attributes and user behavior data; the behavior quantification module is used for quantifying operation behaviors of the user; the commodity category screening module is used for screening categories of all commodities; the matrix filling module performs matrix filling by using a naive Bayesian algorithm, and preliminarily predicts scores of unoperated commodities; the user clustering module is used for clustering the users by utilizing a binary K-means algorithm based on a density division crit