Resource recommendation method and device

Abstract

The invention provides a resource recommendation method and device. The method comprises the following steps of: respectively inputting a marketing resource training sample and a customer resource training sample into a resource classification model to carry out training so as to obtain a marketing resource classification result and a customer resource classification result; and inputting the marketing resource classification result and the customer resource classification result into a similarity model to carry out training so as to output a plurality of matching results as recommendations. According to the resource recommendation method and device, on one hand, modeling training is carried out through machine learning to intelligently classify marketing resources and customer resources,and the classification results are intelligent matched, so that the problem of cold starting is solved, and more accurate recommendation relative to existing scheme is realized; and on the other hand,a large amount of data information related to customers or potential customers is mined through intelligently classifying customer resources, so that data assets are created while CRM accurate recommendation is completed.

Description

technical field [0001] The present application relates to the technical field of customer relationship management, in particular to a resource recommendation method and device. Background technique [0002] With the continuous expansion of the scale of data and information in the era of big data and mobile Internet, people have gradually entered the era of information overload from the era of information scarcity. In this era, the types and numbers of customer resources in customer relationship management (CRM) are growing rapidly, and the group of customers or potential customers is very large. It takes a lot of time for sales to match customers with the required services. , customers do not know how to find and explore their own resource advantages. Therefore, how to effectively manage and excavate complex CRM customer relationship resources, intelligently classify and match relevant resources, and finally achieve the most reasonable allocation and utilization of resource...

AI Technical Summary

Problems solved by technology

[0006] The above two methods have the following defects: on the one hand, there is a cold start problem, and it is impossible to accurately recommend new customer resources and sales resources; on the other hand, they do not consider that the characteristics of customer resources and sales resources are constantly changing, and cannot Tracking customer feedback or behavior in time to evaluate the recommendation effect results in the inability to optimize the matching of resources, relevant superior resources cannot be matched to customers in a timely manner, and enterprises cannot provide accurate and high-quality services to users

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