User tag extraction method and device based on embedded vector and electronic equipment

Abstract

The invention discloses a user tag extraction method and device based on an embedded vector and electronic equipment, and the method comprises the steps: obtaining the embedded vector of full-amount user equipment data after full-amount user authorization, and generating training data; training a plurality of corresponding candidate clustering models through the training data and a plurality of candidate clustering center numbers; performing tuning processing on the candidate clustering model according to tuning data containing positive and negative samples to obtain an optimal clustering model; and clustering the users based on the optimal clustering model, and extracting user tags. The optimal clustering model can perform a layer of semantic clustering on the embedded vector, so that the vector distance between the users is utilized, the semantic similarity between the users is fully mined, the users are clustered, the accuracy of user tag extraction is ensured, misjudgment on the value, willingness and risk of the users is avoided, and the user experience is improved. Especially for misjudgment of user risks, the service security is improved.

Description

technical field [0001] The present invention relates to the technical field of computer information processing, in particular to a method, device, electronic equipment and computer-readable medium for extracting user tags based on embedding vectors. Background technique [0002] With the advent of the era of Internet big data, Internet companies hope to dig out highly accurate, high-coverage, and multi-dimensional label information from massive data to describe users, so as to distinguish the value, willingness, and risks of potential users. [0003] In the prior art, user data is converted into embedding vectors and input into machine learning models (such as marketing models, risk models, etc.), and user descriptions are obtained through these models. However, these models use features such as embedding vectors directly, and cannot make full use of the rich information hidden in the embedding vectors to mine the semantic similarity between users, which affects the accuracy...

AI Technical Summary

Problems solved by technology

However, these models use features such as embedding vectors directly, and cannot make full use of the rich information hidden in the embedding vectors to mine the semantic similarity between users, which affects the accuracy of user characterization, resulting in the loss of user value and willingness. Misjudgment of risk and risk, especially misjudgment of user risk, will cause hidden dangers to business security

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