Enterprise service recommendation method based on enterprise feature propagation

A technology of enterprise service and recommendation method, applied in the field of deep learning, it can solve the problems of heavy workload of meta-path, unable to fully reflect business requirements, and difficult to dig deeper associations.

Active Publication Date: 2021-09-03
SHANDONG ARTIFICIAL INTELLIGENCE INST +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in these recommendation algorithms, ordinary interaction information and auxiliary information are used, but in enterprise service recommendation, it is difficult to map the characteristics of enterprises and the associations between enterprises.
The workload of manually designi

Method used

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  • Enterprise service recommendation method based on enterprise feature propagation

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0058] The fields contained in the enterprise attribute table C in step a) are: enterprise name, business status, industry, enterprise type, business scope, business type, number of business licenses, number of trademarks, number of copyright certificates, number of patents, number of certifications, expiration Number of business licenses, number of expired trademarks, number of expired copyright certificates, number of expired patents, number of expired certifications, number of abnormal corporate tax payments, number of abnormal operations, number of administrative penalties, number of tax arrears records, equity capital, and chattel mortgages; The service attribute table S includes fields: service number, service type, and service price; the service interaction record table R includes fields: enterprise name, service number.

Embodiment 2

[0060]The standardization process in step a) is: filter the enterprises and services that are not in the service interaction record table R in the enterprise attribute table C and service attribute table S, and use the enterprise attribute for the missing value in the enterprise attribute table C The mean value difference processing of this field in table C, for the category missing in the enterprise attribute table C, use the mode supplement of the same industry corresponding to this field.

Embodiment 3

[0062] The characteristics in step b) include the operating status of the enterprise, the industry, enterprise type, business scope, and business type.

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Abstract

An enterprise service recommendation method based on enterprise feature propagation performs feature propagation on enterprises in an interaction record by using a knowledge graph associated between enterprise features and a target service, automatically mines enterprise association paths, depicts associated features between the enterprises, and combines the associated features with the enterprise features. And the new loss function is combined with deep learning to obtain the interaction probability of the enterprise and the service, so that the problem of poor recommendation effect caused by deviation from the relationship between the enterprises due to the fact that a general framework only uses interaction data and basic information can be solved, and accurate recommendation of the service scheme of the enterprise is realized. Through automatic mining of relationships between enterprises, discovery of association paths between the enterprises, interaction prediction scoring of enterprise features, and feature propagation of the enterprises in interaction records by target services and an interaction framework, the problem that the enterprises are difficult to select service schemes is solved.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to an enterprise service recommendation method based on enterprise feature propagation. Background technique [0002] With the rapid development of the economy, small, medium and micro enterprises have become an important force in the current national development. They play an irreplaceable role in improving people's livelihood, promoting employment, and stimulating the economy. They have rapidly changing businesses and imperfect enterprise service supply systems. Enterprise services play an important role in the development of small, medium and micro enterprises. In the face of massive enterprise services, it is difficult to accurately find suitable enterprise service solutions, so the intelligent recommendation of enterprise service solutions is particularly important. [0003] In previous recommendation systems, the main traditional recommendation algorithms have been wide...

Claims

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Application Information

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IPC IPC(8): G06F16/28G06F16/2458G06Q50/10
CPCG06F16/288G06F16/2465G06Q50/10
Inventor 王英龙张翰中舒明雷周书旺刘照阳
Owner SHANDONG ARTIFICIAL INTELLIGENCE INST
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