Social-label-based method for optimizing personalized recommendation system

A technology of social labeling and optimization methods, which is applied in the field of personalized recommendation systems, can solve problems such as K-nearest neighbor model recommendation performance defects, and achieve the effects of making up for data sparsity, improving recommendation performance, and making up for cold start problems

Inactive Publication Date: 2012-03-14
北京天石和合文化传播有限责任公司
View PDF4 Cites 37 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In the research field of personalized recommendation system, the K-nearest neighbor model is the most convenient, simple and mature method in the collaborative filtering recommendation system. However, the recommendation performance of a single K-nearest neighbor model is defective. Research on optimization is very active, inc...

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
  • Social-label-based method for optimizing personalized recommendation system
  • Social-label-based method for optimizing personalized recommendation system
  • Social-label-based method for optimizing personalized recommendation system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0031] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0032] The present invention is a user- and item-oriented personalized recommendation system optimization method based on social tags. First, the user-item social tag matrix T=|U|×|I| and the user-item scoring matrix R=| U|×|I| is used as the basic matrix of the K-nearest neighbor recommendation model; then the basic matrix is ​​processed by the K-nearest neighbor recommendation model to obtain the item set similarity ISim(i n ), user set similarity UTSim(u m ); Then from the itemset similarity ISim(i n ) and user set similarity UTSim(u m ) to select the previous item with the highest similarity, and get the neighbor user-target item score r(u′, i n ), target user-neighbor item score r(u m , i′); Finally, the target user u is obtained by using the weighted average method m For the target item i n prediction score.

[0033] In the present invention, ite...

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 discloses a social-label-based method for optimizing a personalized recommendation system. In the method, social label similarity and score similarity are adopted and applied to calculation of a user-and-project oriented K-nearest neighbor model, and then a user and a project of a K-nearest neighbor are used for calculating a prediction score of the project by the user at the same time. Because the label similarity and the score similarity are adopted in the method at the same time, so that the K-nearest neighbor calculation of the user and the project is more accurate, the recommendation accuracy is obviously higher than that obtained by singly adopting the score similarity, and a cold-start problem based on a label similarity model can be solved. Therefore, a data sparsityproblem can be solved by using a user-and-project oriented recommendation model, and the recommendation accuracy is also obviously higher than that of a conventional user-oriented recommendation model and a project-oriented recommendation model.

Description

technical field [0001] The present invention relates to a personalized recommendation system applicable to e-commerce information, more particularly, to a user- and project-oriented personalized recommendation system optimization method based on social tags. Background technique [0002] In the process of rapid development of e-commerce, personalized recommendation technology is indispensable. Personalized recommendation is based on user's preferences and interests. Personalized recommendation system recommends items that he may be interested in to the user, thereby promoting the increase of sales. Most of the personalized recommendation systems use the collaborative filtering method to provide personalized recommendation information. In the collaborative filtering recommendation system, the user's interest in the item is quantified as the user's rating on the item. In a given user set U={u 1 , u 2 ,...,u c ,...u m} and itemset I = {i 1 , i 2 ,...,i a ,... i n}, the ...

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 欧阳元新秦思思张秦熊璋
Owner 北京天石和合文化传播有限责任公司
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