Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

A One-Class Collaborative Filtering Method Fused with Personality Traits and Item Labels

A collaborative filtering, single-classification technology, applied in special data processing applications, instruments, data processing applications, etc., can solve the problems of cold start of new users, and achieve the effect of overcoming cold start, overcoming cold start problems, and high reliability

Active Publication Date: 2021-06-29
HEFEI UNIV OF TECH
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0013] In order to overcome the shortcomings of the existing single-category collaborative filtering, the present invention proposes a single-category collaborative filtering method that combines personality traits and item tags, in order to use personality traits and item tags as additional information to identify missing data. Mixed positive and negative data to effectively solve the problem of data sparsity and cold start of new users, thereby improving the accuracy of personalized recommendations

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
  • A One-Class Collaborative Filtering Method Fused with Personality Traits and Item Labels
  • A One-Class Collaborative Filtering Method Fused with Personality Traits and Item Labels
  • A One-Class Collaborative Filtering Method Fused with Personality Traits and Item Labels

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0042] In this example, if figure 1 As shown, a single-category collaborative filtering method that combines personality traits and item tags is carried out in the following steps:

[0043] Step 1. Use the two-dimensional table R={u,i} to represent the user’s behavior records on items, use the two-dimensional table P={u,p} to represent the user’s personality trait data, and use the two-dimensional table T={i,tag } represents the tag data of the item; among them, u={u 1 ,u 2 ,...,u n ,...,u |N|} represents the set of users, u n Indicates the nth user, n=1,2,...,|N|, |N| indicates the total number of users; i={i 1 ,i 2 ,...,i m ,...,i |M|} represents a collection of items, i m Indicates the mth item, m=1,2,..., |M|, |M| indicates the total number of items; p={p 1 ,p 2 ,...,p n ,...,p |N|}Represents the information set of user personality traits, p n Indicates the nth user u n personality traits and have: Indicates the nth user u n The jth personality trait of ...

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 single-category collaborative filtering method that combines personality traits and item tags, including: firstly calculating the similarity between users based on user personality traits, and calculating the user's preference for items based on the similarity; then calculating based on the item tags The degree of user preference for items; followed by the fusion of personality-based user preference for items and user preference for items based on item tags to obtain the total preference degree of users for items, and use the total preference degree to construct a matrix decomposition model; finally, according to the model Make recommendations. The present invention uses personality traits and item labels as additional information to identify positive and negative examples mixed together in missing data, thereby effectively solving the data sparse problem and the cold start problem of new users, and then improving the accuracy of personalized recommendation.

Description

technical field [0001] The invention belongs to the field of electronic commerce, in particular to a matrix decomposition method (PTMF) combining personality traits and item labels. Background technique [0002] With the development of information technology, the expansion of information resources and the rapid development of e-commerce, it has become a difficult and expensive obstacle for users to find product information they are interested in; Effectively improving the purchase rate of users has become their primary consideration. Recommender systems can overcome this obstacle by providing users with personalized items, products or services that meet user needs. Collaborative filtering technology is one of the earliest and most successful technologies for personalized recommendation applications. It can provide technical support for users' purchase decisions based on the similarity between items or users. Collaborative filtering has been extended and practically applied...

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
Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/06G06F16/9535
CPCG06Q30/0625G06Q30/0631G06F16/9535
Inventor 孙见山徐东姜元春刘业政孙春华任德源刘雅珏
Owner HEFEI UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products