Method and system for intelligent recommendation of service resource combination based on attribute multi-layer association

Abstract

The invention relates to a method for intelligent service resource combination recommendation based on attribute multilevel association. The method includes: respectively extracting user consumption records, service resource names and service resource attributes from a user transaction library and a service resource library; generating association rules based on the service attributes; generating multi-level association rules based on service attribute generalization; parsing user requirements to generate associated service resources based on attribute description; and performing multi-attribute matching for the associated service resources based on the attribute description, mapping to generate specific service resources, combining according to needed service resources of each link of a process, and returning results to users. Different from a mode that association rules are established purely on the basis of whether service resource instances are transacted or not in the past, the method for mining the association rules based on the service attributes is characterized in that the service attributes are included in a mining category to form the service resource association rules based on the attribute description, and more accurate service resource combinations are recommended to users.

Description

technical field [0001] The present invention relates to a service resource combination recommendation technology, in particular to a service resource combination intelligent recommendation method and system based on attribute multi-layer association. Background technique [0002] With the continuous increase in the types and quantities of service resources and the continuous enhancement of personalization, the choices provided to users become more and more diverse. However, the diversification of services also has disadvantages. It becomes more and more difficult for users to find what they need or potentially interested services from a large number of services. Only by using data mining methods to discover knowledge, realize service resource aggregation, reduce user information search costs, and relieve users from heavy search operations can the user experience be improved, and operators can truly be user-centered, thus standing in the future. defeated. [0003] Among the...

AI Technical Summary

Problems solved by technology

Although the former can complete multi-layer and cross-level frequent rule mining, it only considers the source data at the same level; the latter uses the top-down method to mine layer by layer, but does not support cross-level mining

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