A personalized recommendation method based on the combination of content and collaborative filtering

A technology of collaborative filtering and recommendation methods, which is applied in the directions of instruments, computing, and electrical digital data processing, etc., to achieve the effects of reducing complexity, improving accuracy, and reducing matrix dimensions

Active Publication Date: 2021-11-02
NANJING UNIV OF POSTS & TELECOMM
View PDF6 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The existing content-based and collaborative filtering recommendation algorithms have their own defects, especially the cold start problem of collaborative filtering and the singleness of content-based recommendation, which cannot make these two recommendation systems achieve the expected recommendation effect.

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 personalized recommendation method based on the combination of content and collaborative filtering
  • A personalized recommendation method based on the combination of content and collaborative filtering
  • A personalized recommendation method based on the combination of content and collaborative filtering

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0052] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0053] Such as figure 1 As shown, a personalized recommendation method based on the combination of content and collaborative filtering. The method is divided into two stages of training and personalized recommendation.

[0054] The training phase includes data collection and preprocessing, similarity clustering of existing users, obtaining virtual user-feature attribute matrix, new item-feature attribute matrix, virtual user-new item pre-recommendation matrix, new item pre-recommendation, new item There are eight sub-steps of score acquisition and matrix decomposition.

[0055] In the part of data collection and preprocessing, the platform collects personal information of users, which is usually gender, age, occupation, etc. User behavior characteristics, usually browsing items, purchasing items and other behaviors, and users' evaluation of item...

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 personalized recommendation method based on the combination of content and collaborative filtering, which is divided into two stages: training and personalized recommendation: in the training stage, existing users-new item pre-recommendation matrix is ​​formed by collecting data information, and personalized recommendation The item recommendation for new users and the further recommendation after the user generates a score are completed in the stage. Compared with the traditional personalized recommendation method, the present invention can solve the cold start problem, predict newly added items, recommend them to users, use collaborative filtering recommendation after pre-recommendation, and improve the diversity and accuracy of recommended items. Moreover, generating virtual users through a clustering algorithm greatly reduces the dimension of the matrix, and at the same time reduces the complexity of calculation.

Description

technical field [0001] The invention relates to the technical field of personalized recommendation of big data, in particular to a personalized recommendation method based on the combination of content and collaborative filtering. Background technique [0002] In the information age, information has also become a resource. The rapid development of the information age has accelerated the progress of the whole world and the pace of people's lives, and the effectiveness of time utilization has become the key. And contrary to this, the explosive amount of data is an urgent problem to be solved. In this context, the rationality of the emergence and development of the recommendation system is well known. It is the goal of the recommendation system to select items for the user's preferences in a short period of time. [0003] The existing content-based and collaborative filtering recommendation algorithms have their own defects, especially the cold start problem of collaborative...

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): G06F16/9536G06K9/62
CPCG06F16/9535G06F18/23
Inventor 施沈池蒋琳王玉峰
Owner NANJING UNIV OF POSTS & TELECOMM
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