Multi-dimensional feature construction method and device, electronic device and storage medium

Abstract

The invention provides a multi-dimensional feature construction method and device, an electronic device and a storage medium. The multi-dimensional feature construction method comprises the followingsteps: when a feature construction instruction is received, acquiring historical access data of the target application program, detecting current access data of the target application program, calculating the similarity between the historical access data and the current access data by adopting a K-L divergence algorithm; obtaining first feature data, obtaining current time and determining a targettime slice to which the current time belongs, calling a target activity degree corresponding to the target time slice from pre-stored activity degree data, processing the target activity degree; acquiring second feature data, further acquiring third feature data acquired by a configuration sensor, performing normalization processing on the first feature data, the second feature data and the thirdfeature data, and performing data analysis to obtain multi-dimensional features, so that the description of user features is more comprehensive, and the flexibility is higher.

Description

technical field [0001] The present invention relates to the technical field of data analysis, in particular to a multi-dimensional feature construction method, device, electronic equipment and storage medium. Background technique [0002] In the prior art solution, for the application program on the smart terminal, the user features are mainly constructed through the data structure of the low-dimensional vector, that is, the attribute feature of a single user is mapped into a one-dimensional vector based on a single primary key. [0003] In this case, once there is a surge in user characteristics, the attribute characteristics of a single user will not be able to meet the demand. Moreover, in order to obtain more accurate results, many tasks now require feature extraction based on multi-dimensional and ever-changing data. Obviously, , the user characteristics described by the existing technical solutions are not only one-sided, but also have low flexibility. Contents of th...

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Problems solved by technology

[0003] In this case, once there is a surge in user characteristics, the attribute characteristics of a single user will not be able to meet the demand. Moreover, in order to obtain more accurate results, many tasks now require feature extraction based on multi-dimensional and ever-changing data. Obviously, , the user characteristics described by the existing technical solutions are not only one-sided, but also have low flexibility

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