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

A technology of non-negative matrix decomposition and recommendation method, which is applied in marketing and other directions, and can solve problems such as inability to effectively deal with new users

Inactive Publication Date: 2014-02-05
ZHEJIANG UNIV
View PDF5 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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 soc

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Personalized product recommendation method based on combined non-negative matrix decomposition
  • Personalized product recommendation method based on combined non-negative matrix decomposition
  • Personalized product recommendation method based on combined non-negative matrix decomposition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0030] With reference to accompanying drawing, further illustrate the present invention:

[0031] A product recommendation method based on joint non-negative matrix factorization:

[0032] 1. The method comprises the following steps:

[0033] 1) Grab data information from the Internet, including users' ratings on purchased products, friendship between users, and users' text comments on purchased products;

[0034] 2) Transform the data information into a data matrix, and the data information of each user is one of the row vectors;

[0035] 3) Using the method of joint non-negative matrix decomposition, the original data matrix is ​​decomposed into multiple data matrices in low-dimensional space;

[0036] 4) According to the data matrix in the low-dimensional space, estimate the ratings of each user for all unpurchased products, and recommend products according to the ratings.

[0037] The user's rating of the purchased product and the friendship between users described in s...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06Q30/02
Inventor 王灿王哲李平卜佳俊陈纯何占盈
Owner ZHEJIANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap