Interpretable commodity recommendation method based on fine-grained data

A commodity recommendation and fine-grained technology, applied in data processing applications, electrical digital data processing, special data processing applications, etc., can solve problems such as no use, limited algorithms, lack of attention to emotional factors, etc.

Inactive Publication Date: 2019-07-26
JILIN UNIV
View PDF5 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] 1. Insufficient use of data, failure to fully mine and utilize various information reflected by users in product reviews
[0006] The user's comments on the product will reflect the product aspect that the user cares about, and the verbs and descriptors used on the aspect express the emotional tendency of the aspect. Most algorithms do not use this aspect whe

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
  • Interpretable commodity recommendation method based on fine-grained data
  • Interpretable commodity recommendation method based on fine-grained data
  • Interpretable commodity recommendation method based on fine-grained data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0067] Below in conjunction with accompanying drawing, the present invention is described in detail:

[0068] The problem to be solved by the interpretable product recommendation method based on fine-grained data in the present invention is to start from the fine-grained data that the current technology lacks attention, and propose to uniformly and standardizedly represent the review text through a syntactic analysis tree and extract key information from it to obtain details. The method of granular data proposes a method of personalized recommendation and explanation for users based on the product aspects in fine-grained data and the user's emotional tendency, which improves the accuracy of recommendation and the interpretability of recommendation results.

[0069] refer to figure 1 , in order to achieve the purpose of interpretable product recommendation method based on fine-grained data, a computer program was compiled by itself, which includes three functional modules, incl...

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 an interpretable commodity recommendation method based on fine-grained data. The method aims to overcome the problems that in the prior art, the commodity comment content is insufficiently utilized, the interpretability is weak, and the effect of a user on commodity recommendation is not fully utilized due to the emotional tendency displayed in comments. The method comprises the steps that 1, fine-grained data of the user on the commodity comments are obtained through a data processing module; 2, a recommendation chain establishing module generates a recommendation chain of the to-be-recommended commodity for the target user according to the fine-grained data; and 3, the recommendation sequence is interpreted by the recommendation generation module according to therecommendation chain.

Description

technical field [0001] The present invention relates to an explainable recommendation method in the field of product recommendation, more precisely, the present invention relates to an explainable product recommendation method based on fine-grained data. Background technique [0002] The recommendation system has been widely used as a method of information filtering, which provides users with more humanized services and brings considerable economic benefits to businesses. The explainability of the recommendation system is defined as explaining the working principle of the recommendation system, making the system transparent, allowing users to understand when the system makes mistakes (testability), helping users make fast and high-quality decisions (effectiveness), affecting or Persuade users to choose products (persuasion) and improve user acceptance of recommended products (satisfaction). [0003] At present, there are mainly four types of methods for implementing interpr...

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): G06Q30/06G06F17/27
CPCG06Q30/0631G06F40/211
Inventor 马涪元王英王鑫孙玉东陈文祺肖旻昊
Owner JILIN UNIV
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