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User behavior processing method, storage medium and related equipment

A behavior and user technology, applied in the Internet field, can solve the problems of poor user experience and low accuracy, and achieve the effect of improving user stickiness, improving prediction accuracy, and improving user experience.

Active Publication Date: 2020-09-18
GUANGZHOU SHIYUAN ELECTRONICS CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present application provides a user behavior processing method, storage medium and related equipment, so as to at least solve the technical problems in related technologies that the accuracy of user behavior prediction is not high and the user experience is poor

Method used

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  • User behavior processing method, storage medium and related equipment
  • User behavior processing method, storage medium and related equipment
  • User behavior processing method, storage medium and related equipment

Examples

Experimental program
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Embodiment 1

[0037] The embodiment of the present application provides a user behavior processing method, which is applied to electronic devices, such as computer terminals, smart phones, tablet computers, etc., but is not limited thereto, and the present application does not specifically limit it.

[0038] figure 1 is a flow chart of the first user behavior processing method according to an embodiment of the present application, such as figure 1 As shown, the method includes the following steps:

[0039] Step S102, acquiring behavior data of multiple objects;

[0040] The object in the above steps may be a user using an APP installed on an electronic device, and the APP may be an Internet APP. For example, the class optimization master APP is used as an example for illustration, but it is not limited thereto.

[0041] Different users usually perform different behavioral operations when using the same APP. In order to accurately predict the user behavior of each user, the historical beha...

Embodiment 2

[0050] like figure 2 As shown, the method includes the following steps:

[0051] Step S201, acquiring behavior data of multiple objects;

[0052] Step S202, matching the behavior data with the preset behavior data;

[0053] The preset behavior data in the above steps can be predefined noise behaviors and stains. For example, taking the class optimization master app as an example, noise behaviors can include but are not limited to: modifying nicknames, setting avatars, etc.

[0054] Step S203, if the behavior data is successfully matched with the preset behavior data, the behavior data is removed to obtain the cleaned behavior data;

[0055] In an exemplary embodiment of the present invention, after acquiring the historical behavior data of all users, the acquired historical behavior data may be cleaned based on preset behavior data to obtain clean behavior data.

[0056] Step S204, segmenting the cleaned behavior data to obtain multiple behavior sequences;

[0057] In an ...

Embodiment 3

[0089] like image 3 As shown, the method includes the following steps:

[0090] Step S301, acquiring behavior data of multiple objects;

[0091] Step S302, constructing a directed graph based on the behavior data of multiple objects, wherein the directed graph includes: multiple nodes, edges connected between any two nodes, and edge weight values, and the nodes are used to represent behavior data The different behaviors of , and the edge is used to represent the transfer relationship between two behaviors connected by the edge;

[0092] Step S303, search the directed graph, determine at least one behavior path from non-target behavior to target behavior, and the weight value of each behavior path, wherein the number of different behaviors contained in the behavior path is less than or equal to the preset number ;

[0093] Step S304, acquiring the target behavior sequence of the target object;

[0094] The target object in the above steps can be the user currently using th...

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Abstract

The embodiment of the invention discloses a user behavior processing method, a storage medium and related equipment, and the method comprises the steps: obtaining the behavior data of a plurality of objects; constructing a directed graph based on the behavior data of the plurality of objects, the directed graph comprising a plurality of nodes, edges connected between any two nodes, and weight values of the edges, the nodes being used for characterizing different behaviors in the behavior data, and the edges being used for characterizing a transfer relationship between two behaviors connected through the edges; and searching the directed graph, and determining at least one behavior path for transferring the non-target behavior to the target behavior and a weight value of each behavior path,the number of different behaviors contained in the behavior path being less than or equal to a preset number. Therefore, the embodiment of the invention can achieve the technical effects of improvingthe prediction accuracy, improving the user experience feeling and improving the user stickiness, thereby solving the technical problems of low processing accuracy of user behaviors and poor user experience feeling in related technologies.

Description

technical field [0001] The present application relates to the Internet field, and in particular, relates to a user behavior processing method, storage medium and related equipment. Background technique [0002] At present, user behavior analysis and behavior prediction usually use classification models to predict whether users will perform specific behaviors by extracting user features. The implementation method is as follows: apply extraction and apply Gradient Boosting Decision Tree (Gradient Boosting Decision Tree, GBDT) as a training model; use mathematical and statistical models to combine user purchase behavior, process, influencing factors and other theories to build a user prediction model; build based on target behavior sequence data Based on the latent factor selection model, the user's purchase decision in a purchase cycle is predicted; mathematical models such as linear regression and logarithmic models are used to predict the probability of multiple purchases by...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06F11/34
CPCG06Q30/0202G06F11/3438Y02D10/00
Inventor 黄昕虹
Owner GUANGZHOU SHIYUAN ELECTRONICS CO LTD
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