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

Recommendation algorithm based on knowledge graph

A knowledge graph and recommendation algorithm technology, applied in computing, neural learning methods, instruments, etc., can solve the problems of sparse interactive data and inaccurate recommendation results, and achieve the effect of enhancing recommendation performance and improving recommendation effect.

Active Publication Date: 2022-05-13
浙江辰时科技集团有限公司
View PDF13 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a recommendation algorithm based on knowledge graphs, which solves the problem of inaccurate recommendation results caused by traditional recommendation algorithms due to sparse interaction data and cold start

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
  • Recommendation algorithm based on knowledge graph
  • Recommendation algorithm based on knowledge graph
  • Recommendation algorithm based on knowledge graph

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0054] The present invention will be specifically introduced below in conjunction with the accompanying drawings and specific embodiments.

[0055] This application discloses a recommendation algorithm based on knowledge graphs, which includes the following steps: constructing knowledge graphs based on application scenarios; constructing KGRN models based on GNN; inputting knowledge graphs into KGRN models to obtain embedding vectors and expressing users' preferences for items according to the embedding vector output The degree of recommendation indicators; according to the recommendation indicators to recommend operations to users. The recommendation algorithm based on the knowledge map of this application constructs a high-quality knowledge map according to the specific scene recommended. After obtaining the historical data of user-item interaction, user-related information, and item-related attributes in the recommendation scenario, we perform knowledge extraction to obtain...

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 recommendation algorithm based on a knowledge graph. The recommendation algorithm comprises the following steps: constructing the knowledge graph based on an application scene; constructing a KGRN model based on the GNN; inputting the knowledge graph into a KGRN model to obtain an embedded vector, and outputting a recommendation index which expresses the preference degree of the user to the article according to the embedded vector; and performing recommendation operation to the user according to the recommendation index. According to the recommendation algorithm based on the knowledge graph, the recommendation performance is enhanced in a mode of fusing the knowledge graph and the recommendation system. And the embedding vectors of the nodes are introduced into the click rate prediction model, so that the recommendation effect is further improved.

Description

technical field [0001] The invention belongs to the technical field of computers, and in particular relates to a recommendation algorithm based on a knowledge map. Background technique [0002] Traditional recommendation algorithms are mainly divided into three categories, including content-based recommendation algorithms, collaborative filtering-based recommendation algorithms, and hybrid recommendation algorithms. The recommendation algorithm based on collaborative filtering uses the feedback data of users and item history to mine the correlation between users and items themselves, and make recommendations based on this. This type of algorithm has domain-independent characteristics, so it is widely used. However, collaborative filtering algorithms have data sparsity and cold start problems. The content-based recommendation algorithm can effectively alleviate the cold start problem, and solve the cold start problem of new items by constructing portraits for the content of...

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): G06F16/36G06F16/9535G06N3/04G06N3/08
CPCG06F16/367G06F16/9535G06N3/04G06N3/08
Inventor 袁晓军贾帅琪黄浩秦浪
Owner 浙江辰时科技集团有限公司
Features
  • Generate Ideas
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

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

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com