Service recommendation method oriented to service supply chain network

Abstract

The invention discloses a service recommendation method oriented to a service supply chain network. The method includes the steps that firstly, a transfer matrix is established according to the linkage relationship among all nodes in the service supply chain network, and credit values of all the nodes are calculated according to the transfer matrix; then, similarity of credit enhancement between every two nodes is calculated, and neighbors of all the nodes are established; finally, for users needing service recommendation, the service quality evaluation values of services needed by the users are predicted according to the neighbors of the corresponding nodes, and the services with high service quality evaluation values are recommended to the users. By the adoption of the service recommendation method, the service quality evaluation values of the services needed by the users can also be successfully predicted even when historical data are insufficient or the historical data of part of the nodes do not exist, the problems of data sparsity and cold boot in the existing collaborative filtering technology are solved, and service recommendation accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of supply chain configuration, and in particular relates to a service recommendation method oriented to a service supply chain network. Background technique [0002] In the field of supply chain configuration, it is generally believed that its development trend is to increasingly emphasize the sharing and integration of various external high-quality services (such as resources and knowledge). The exponential growth of the Internet and the accelerated globalization of manufacturing enterprises have significantly affected the interoperability of cross-enterprise collaboration services. For example, the SOA architecture and the corresponding Web services not only provide a dynamic implementation for the communication between manufacturing enterprises and their partners (including suppliers and customers) through the seamless integration of various complex distributed manufacturing resources and knowledge techn...

AI Technical Summary

Problems solved by technology

However, the recommendation effect of collaborative filtering technology is easily affected by data sparsity and cold start problem.

When historical data is insufficient to find similar users or items, data sparsity problems will arise at this time

When a new user or item does not have historical data, it is impossible to find similar users or similar items, which will cause a cold start problem

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