Personalized product recommendation method based on combined non-negative matrix decomposition

Abstract

The invention provides a personalized product recommendation method based on combined non-negative matrix decomposition. The method comprises a step of capturing data information which comprises the score of a purchased product by a user, the friend relation between users and a text evaluation of the purchased product by the user in the Internet, a step of converting the data information into a data matrix, wherein the data information of each user is a row vector, a step of using a method of combined non-negative matrix decomposition to separate the original data matrix into a plurality of data matrices under a plurality of low-dimensional spaces, and a step of estimating the scores of all products which are not purchased by each user according to the data matrices under the low-dimensional spaces and carrying out product recommendation according to high and low scores. The method has the advantages that a user structure relation and product evaluation information in a social network are comprehensively considered, a problem that a traditional method can not effectively process a new user is solved, and the effective product recommendation is carried out on a new user who has not purchase any product.

Description

technical field [0001] The invention relates to the technical fields of non-negative matrix factorization and product recommendation, especially considering the complex social network structure of users and the product recommendation work of joint non-negative matrix factorization. Background technique [0002] With the rapid development of the Internet, more and more physical goods are turning to online sales. Online sales saves the investment in stores for physical sales, reduces the labor cost of store maintenance, and at the same time it is easier to get rid of geographical restrictions and sell products to all parts of the country and even other countries. However, in the face of a large number of potential users, how to make reasonable product recommendations for specific groups of people has become one of the most effective ways to expand product revenue. At the same time, in addition to targeted marketing of products, recommendation algorithms are also widely used i...

AI Technical Summary

Problems solved by technology

[0004] The invention proposes a method based on joint non-negative matrix decomposition, which comprehensively considers the user structure relationship and product evaluation information in the social network, and solves the problem that the traditional method cannot effectively deal with new users. For new users who have not purchased any products Users make effective product recommendations

Images