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Data processing method and device, computer readable storage medium and electronic equipment

A data processing and data technology, applied in the field of data processing, can solve problems such as the reduction of data processing efficiency

Pending Publication Date: 2020-05-15
TENCENT TECH (SHENZHEN) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] During the research and practice of the prior art, the inventors of the present application found that although the prior art provides a means of manually classifying multiple behavioral feature data, manual classification will make the data processing more difficult. greatly reduced efficiency

Method used

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  • Data processing method and device, computer readable storage medium and electronic equipment
  • Data processing method and device, computer readable storage medium and electronic equipment
  • Data processing method and device, computer readable storage medium and electronic equipment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0073] In this embodiment, description will be made from the perspective of a data processing device. Specifically, the data processing device may be integrated into a server provided with a storage unit and equipped with a microprocessor and capable of computing.

[0074] A data processing method, comprising: collecting a user time series sample set, and generating initial positive sample data and initial negative sample data from the user time series sample set; inputting the initial positive sample data and the initial negative sample data into a preset neural network model for the first First training, obtain the preset neural network model after the first training; identify the user time series sample set according to the preset neural network model after the first training, and determine the target positive sample data and mutation negative sample data; the target positive sample data and the mutation negative sample data are input to the initialized preset neural network...

Embodiment 2

[0138] According to the method described in Embodiment 1, an example will be given below for further detailed description.

[0139] In this embodiment, description will be made by taking the data processing apparatus specifically integrated in a server as an example, for details, refer to the following description.

[0140] see image 3 , image 3 Another schematic flowchart of the data processing method provided in the embodiment of the present application.

[0141] The method flow may include:

[0142] In step 201, the server sequentially selects the first target user time-series sample from the user time-series sample set in chronological order, and selects the first target user formed by a preset number of consecutive user time-series samples based on the first target user time-series sample A sequence of time series samples.

[0143] Among them, the user time-series sample set can be expressed as {t1, t2, t3, ..., tk}, assuming that the user time-series sample set is ...

Embodiment 3

[0184] In order to better implement the data processing method provided in the embodiment of the present application, the embodiment of the present application further provides a device based on the above data processing method. The meanings of the nouns are the same as those in the above data processing method, and for specific implementation details, please refer to the description in the method embodiments.

[0185] see Figure 5a , Figure 5a It is a schematic structural diagram of a data processing device provided in an embodiment of the present application, wherein the data processing device may include a generation unit 301 , a first training unit 302 , a determination unit 303 , and a second training unit 304 .

[0186] The generation unit 301 is configured to collect a user time-series sample set, and generate initial positive sample data and initial negative sample data from the user time-series sample set.

[0187] In some embodiments, such as Figure 5b As shown...

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Abstract

The embodiment of the invention discloses a data processing method and device, a computer readable storage medium and electronic equipment, and the method comprises the steps: collecting a user time sequence sample set, and generating initial positive sample data and initial negative sample data from the user time sequence sample set; inputting the initial positive sample data and the initial negative sample data into a preset neural network model for first training to obtain a first trained preset neural network model; identifying the user time sequence sample set according to the preset neural network model after the first training, and determining target positive sample data and mutation negative sample data; and inputting the target positive sample data and the abrupt change negative sample data into the initialized preset neural network model for second training to obtain a second trained preset neural network model. Through secondary training, the second trained preset neural network model with the sequence division function is obtained, automatic division of the user time series data is achieved, and the data processing efficiency and accuracy are greatly improved.

Description

technical field [0001] The present application relates to the technical field of data processing, and in particular to a data processing method, device, computer-readable storage medium, and electronic equipment. Background technique [0002] With the wide application of the network and the rapid development of terminal technology, the life and work of the entire human society are more and more closely connected with terminal technology. In order to provide users with more intelligent Interactive behavioral feature data, and then analyze user preferences and habits. [0003] In the prior art, the terminal can acquire multiple behavioral feature data that the user interacts continuously on the terminal, for example, acquire multiple behavioral feature data generated by the user's continuous purchase of multiple commodities, and manually divide the multiple behavioral feature data, Behavioral characteristic data of multiple categories are obtained. [0004] During the resear...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/2411G06F18/214Y02D10/00
Inventor 缪畅宇
Owner TENCENT TECH (SHENZHEN) CO LTD
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