Clustering collaborative filtering recommendation system based on singular value decomposition algorithm

Abstract

The invention provides a clustering collaborative filtering recommendation technology based on a singular value decomposition algorithm. The clustering collaborative filtering recommendation technology based on the singular value decomposition algorithm comprises firstly classifying users by using user attributive character values provided by the clustering collaborative filtering recommendation technology based on the singular value decomposition algorithm, and reducing dimension of a user-commodity grade matrix; improving a singular value decomposition (SVD) algorithm which is frequently used in image processing and natural language processing, and using the improved SVD algorithm in a recommendation system; decomposing a grade matrix in a cluster where users are located, and aggregating the decomposed grade matrix so as to fill predicted scores of non-grade items in the grade matrix, calculating similarity of the users in the same cluster by using the filled grade matrix, calculating final predicted scores of a commodity by applying collaborative filtering technologies based on the users and widely applied in the recommendation system, and carrying out final recommendation. The clustering collaborative filtering recommendation technology based on the singular value decomposition algorithm has the advantages of being capable of improving recommendation efficiency of the recommendation system, solving the problems such as data sparsity of the recommendation system, and meanwhile being capable of improving accuracy rate of recommendation of the recommendation system.

Description

technical field [0001] The invention belongs to the field of e-commerce recommendation systems, and specifically relates to a recommendation method that integrates various technologies, such as data mining technology, machine learning technology, natural language processing technology, etc., and realizes the combination of clustering and singular value decomposition (SVD) technology. Background technique [0002] In recent years, with the rapid development of Internet technology, e-commerce has become a new fashion, forming a trend of rapid growth in recent years. E-commerce, which is a new business transaction process produced by the combination of IT technology and business behavior, is the main mode of business operation in the market economy in the 21st century. Through the e-commerce platform, people can enjoy the convenience of purchasing goods without leaving home. and convenience. With the expansion of the transaction scale of the e-commerce platform, people cannot ...

AI Technical Summary

Problems solved by technology

The present invention solves the problem of recommendation efficiency through a clustering algorithm, and uses the SVD algorithm to solve the problem of local data sparsity, thereby improving the recommendation results