A group recommendation method and device based on customer characteristics

Abstract

The invention discloses a grouping recommendation method based on customer characteristics and its device, comprising: obtaining feature information of multiple historical customers and preprocessing it, obtaining data points corresponding to each historical customer, and forming a data set to be grouped; calculating For the density of data points around each data point, select the first N data points with the highest data point density as the initial density peak points; cluster each initial density peak point respectively to obtain each initial cluster; combine each initial cluster with the corresponding Link the recommendation data of the new customer; receive the feature information of the new customer and preprocess it to obtain the data points corresponding to the new customer; determine the initial cluster to which the new customer belongs, and call the recommended data linked by the initial cluster to which it belongs for display. The invention can automatically screen cluster centers according to the data point density of each data point, does not rely on manual selection, reduces the workload of staff, improves the accuracy of clustering and grouping, and has better recommendation effect.

Description

technical field [0001] The present invention relates to the technical field of group recommendation, in particular to a method and device for group recommendation based on customer characteristics. Background technique [0002] In the field of data recommendation technology, the main method is to group by density-based clustering algorithm, and then recommend corresponding data to customers according to the group they belong to, such as bank or mobile business recommendation, website hotspot recommendation, etc. [0003] The main idea of ​​the density-based clustering method is to find high-density areas separated by low-density (sparse) areas. Compared with traditional clustering methods, it can handle the noise in the data set well and reduce the impact of noise on the clustering results. It is also very suitable for processing datasets of various shapes. Among them, the classic density peak clustering algorithm (CFSFDP) is based on the following idea: for a data set, the...

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Problems solved by technology

[0005] However, in the above-mentioned process, the selection of the cluster center is artificially selected based on the distribution of data points in the decision-making diagram. subjectivity, the final grouping accuracy is low, and the recommendation effect is poor

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