A method for constructing a multi-dimensional information portrait of a travel agency user

Abstract

The invention discloses a method for constructing a multi-dimensional information portrait of a travel agency user, which comprises the following steps: acquiring a feature vector of a sample user anduser portrait information; assigning weights to each tag of the sample user; Setting the number k of clusters to be clustered and the similarity threshold theta; according to the tag vector of the sample users, the similarity between each two sample users is calculated respectively; calculating a neighbor matrix A; calculating a link matrix; the similarity measure of every two sample users is calculated and the two sample users with the highest similarity measure are merged. The objective function is used to obtain the best clustering result. Iteration is repeated until k clusters are formed;Grading user portraits. The invention utilizes the clustering algorithm to construct the portrait of the travel agency user, and classifies the portrait into groups, which is favorable for sorting out the commonalities among users, so as to understand the user requirements more, further expand new users, and determine the mode of the tourism product.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to a method for constructing multi-dimensional information portraits of travel agency users. Background technique [0002] With the development and application of big data technology, more and more enterprises and government agencies have an increasing need to accurately locate the characteristics of a person. The most important thing in the process of big data project integration is to target different types of people. Need for precise marketing and personalized solutions. On the one hand, users need to select the products they are interested in from a large amount of product information; on the other hand, enterprises need to accurately sell suitable products to suitable users through modification of product characteristic parameters. Currently commonly used recommendation algorithms include: content-based recommendation, user behavior-based recommendation, hy...

AI Technical Summary

Problems solved by technology

Currently commonly used recommendation algorithms include: content-based recommendation, user behavior-based recommendation, hybrid model-based recommendation, label-based recommendation, etc.; for the operation model with relatively small product volume and large user volume, precise matching is required In , these commonly used recommended methods are not very practical

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