An enterprise service recommendation method based on enterprise feature propagation

Abstract

An enterprise service recommendation method based on enterprise feature dissemination, using the knowledge graph associated with enterprise features, using the target service to perform feature dissemination for enterprises in the interaction records, automatically mining enterprise association paths, and describing the association characteristics between enterprises. Feature combination, using the new loss function combined with deep learning to obtain the interaction probability between the enterprise and the service, can solve the problem of poor recommendation effect caused by the general framework only using the interaction data and basic information and deviating from the relationship between enterprises, and realize the service to the enterprise The program is accurately recommended. Through the automatic mining of inter-enterprise relationships, the discovery of inter-enterprise association paths, and the interactive prediction and scoring of enterprise characteristics, the communication of enterprise characteristics and interaction frameworks in the interaction records by target services is used to solve the problem of difficulty in selecting service solutions for enterprises.

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...

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 designing the meta-path associated with the enterprise is extremely heavy, and it is difficult to dig deeper into the association relationship. The complex business needs of the enterprise cannot be fully reflected, making it impossible to mine the potential services required by the enterprise.

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