Information recommendation method and system based on multi-path optimization matching

Abstract

The invention provides an information recommendation method and system based on multi-path optimization matching. Historical payment information, power utilization information, recommendation serviceinformation and service recommendation information of each client are acquired; based on the payment behavior data and the power consumption behavior data, the data is clustered by using a fuzzy C-means clustering algorithm, clustering features suitable for analyzing customer behaviors from the data are selected, customer behavior theme tags are constructed, and different types of power customer behavior mode portraits are achieved; the electricity consumption behavior data and the payment behavior data of the client are clustered, behavior characteristics are analzyed, behavior label characteristics are set, business characteristic attributes are described, a business label set is formulated, and business portrait construction is completed; according to the method, a bipartite graph of aclient label set and a service label set is constructed, a Hopcroft-Karp algorithm with the maximum bipartite graph matching is applied to matching of clients and services, the matched clients are found, and service information recommendation is completed.

Description

Technical field [0001] The invention belongs to the technical field of business recommendation in the power industry, and relates to an information recommendation method and system based on multi-path optimization and matching. Background technique [0002] The statements in this section merely provide background information related to the present invention, and do not necessarily constitute prior art. [0003] Under the situation of deepening electric power reform, power supply companies are increasingly competing for customer resources. In order to occupy a favorable position in the fierce market competition environment, we will promote the healthy and steady development of enterprises with customer service as the core, and accurately recommend satisfaction to customers. The power business it needs has become a hot issue for power supply companies. [0004] At present, the needs of power customers are becoming diversified, and the customer group is diversifying, which makes it dif...

AI Technical Summary

Problems solved by technology

[0004] At present, the needs of power customers tend to be diversified, and customer groups are diverging, which makes it difficult for power supply companies to fully and accurately grasp customer needs and make accurate recommendations for customers

Therefore, it often happens that customers are not interested in the recommended business, the efficiency of business promotion is half the effort, and there are more and more types of power business. There is very little power business in the market that can meet the needs of all customers.

In the past, in order to recommend business to customers, power supply companies took measures such as regularly pushing business information to customers, publishing business information on major platforms, email marketing, and recommending business to customers based on customer characteristics. Although these measures can effectively reduce the burden of other recommendation service channels pressure, but customer satisfaction has not improved, and the business recommendation process is blind and disturbs customers too much, and the relationship between power supply companies and customers has not been effectively promoted

At the same time, it also causes the information sending pressure of the recommended information platform, occupying information sending traffic and time

Images