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

Graph neural network recommendation method based on multi-aspect enhancement

A neural network and recommendation method technology, applied in the field of data mining, can solve problems such as ignoring user intentions and failing to capture fine-grained

Active Publication Date: 2021-09-10
WUHAN UNIV
View PDF6 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, they ignore the multifaceted user intent embodied in text reviews, resulting in failure to capture fine-grained user preferences.

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
  • Graph neural network recommendation method based on multi-aspect enhancement
  • Graph neural network recommendation method based on multi-aspect enhancement
  • Graph neural network recommendation method based on multi-aspect enhancement

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0069] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0070] The graph neural network recommendation method based on multi-aspect enhancement uses the BERT model to learn the user's aspect emotional characteristics, and constructs a heterogeneous aspect-aware graph. High-order neighbor aggregation is used to capture fine-grained preference features; then a routing-based fusion module is used to adaptively fuse various features to obtain a unified representation of users and items, thereby improving the recommendation performance and interpretability of the recommendation system.

[0071] Such as figure 1 As shown, the present invention discloses ...

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 graph neural network recommendation method based on multi-aspect enhancement. According to the invention, an aspect-enhanced graph neural network framework is defined, and the framework mainly comprises three modules: a feature learning module, an aspect sensing graph module and a routing-based fusion module. The feature learning module learns aspect emotion features and interaction features by using BERT and an embedded layer. The aspect sensing graph module captures fine-grained user preferences and item attributes by constructing a plurality of aspect sensing graphs in parallel. The routing-based fusion module realizes dynamic fusion of aspect preferences by learning distribution of user preferences in different aspects. According to the method, fine-grained user preferences are learned by means of multiple aspect sensing graphs, and unified preference representation is obtained by dynamically fusing multi-aspect preference features by means of a routing mechanism, so that the recommendation performance and interpretability are improved.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to a graph neural network recommendation method based on multi-aspect enhancement in the field of intelligent recommendation. Background technique [0002] The popularization of the Internet and the development of e-commerce present abundant information and diversified products to people, but it is accompanied by a serious problem of information overload. The emergence of information overload makes it difficult for users to find the information they are interested in from massive amounts of data. As an important means to solve information overload, recommendation system has become the core component of many search engines, e-commerce, news and information and other websites. Collaborative filtering [Document 1] is currently the most successful recommendation algorithm. It models the user's historical interaction to capture potential user preferences and item attribu...

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): G06F16/9535G06K9/62G06N3/04G06N3/08
CPCG06F16/9535G06N3/08G06N3/045G06F18/25
Inventor 李晶张晨燕何发智刘东华王明锋常军
Owner WUHAN UNIV
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

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com