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Method and system for explaining recommendation effect, electronic equipment and readable storage medium

A behavioral and user-based technology, applied in the field of data analysis, can solve problems such as the inability to display data layer by layer

Pending Publication Date: 2021-05-18
BEIJING MININGLAMP SOFTWARE SYST CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The disadvantage is that there is no chronological order or correlation between labels, and it cannot be progressively displayed on the upper layer of the data, resulting in the effect of analyzing downwards from a single dimension

Method used

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  • Method and system for explaining recommendation effect, electronic equipment and readable storage medium
  • Method and system for explaining recommendation effect, electronic equipment and readable storage medium
  • Method and system for explaining recommendation effect, electronic equipment and readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] figure 1It is a schematic diagram of the steps of a method for explaining recommendation effects based on user behavior traces provided by the present invention. Such as figure 1 As shown, this embodiment discloses a specific implementation of a method for explaining recommendation effects based on user behavior traces (hereinafter referred to as "method").

[0047] Specifically, the method disclosed in this embodiment mainly includes the following steps:

[0048] Step S1: Obtain relevant data of the user.

[0049] Specifically, user-related data includes: user data, material data, and user behavior data, which can be updated according to the actual situation. For example, user behavior data is generally updated in real time, and every time a user updates a behavior, it will be sent to The recommendation system, or directly connect to the SDK to collect logs; the material data is updated through the API when the customer needs to add a recommended material, or when t...

Embodiment 2

[0067] In combination with the method for explaining recommendation effects based on user behavior traces disclosed in Embodiment 1, this embodiment discloses a specific implementation example of a system for explaining recommendation effects based on user behavior traces (hereinafter referred to as the "system").

[0068] refer to Figure 4 As shown, the system includes:

[0069] Data acquisition module 11: acquire relevant data of the user;

[0070] Model training module 12: training a model according to the relevant data;

[0071] Recommendation module 13: return different recommendation results for different users based on the trained model combined with operating strategies and user real-time behavior;

[0072] Track recording module 14: record the user's behavior track according to the recommendation result;

[0073] Track display module 15: display the behavior track on the front-end interface.

[0074] Specifically, the relevant data in the data acquisition module ...

Embodiment 3

[0080] combine Figure 5 As shown, this embodiment discloses a specific implementation manner of a computer device. The computer device may comprise a processor 81 and a memory 82 storing computer program instructions.

[0081] Specifically, the processor 81 may include a central processing unit (CPU), or an Application Specific Integrated Circuit (ASIC for short), or may be configured to implement one or more integrated circuits in the embodiments of the present application.

[0082] Among them, the memory 82 may include mass storage for data or instructions. For example without limitation, the memory 82 may include a hard disk drive (Hard Disk Drive, referred to as HDD), a floppy disk drive, a solid state drive (SolidState Drive, referred to as SSD), flash memory, optical disk, magneto-optical disk, magnetic tape or universal serial bus (Universal Serial Bus, referred to as USB) drive or a combination of two or more of the above. Storage 82 may comprise removable or non-r...

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Abstract

The invention discloses a method and system for explaining a recommendation effect, an electronic equipment and a readable storage medium, and the method comprises the steps: obtaining related data of a user, including user data, material data and user behavior data; training a model according to the related data; returning different recommendation results for different users based on the trained model in combination with the operation strategy and the real-time behaviors of the users; recording a behavior track of the user according to the recommendation result, wherein the behavior track comprises historical behavior data generated by the user before the recommendation result is returned and active operation behavior data of the user for the recommendation result after the recommendation result is returned; and setting a limiting condition, and displaying the user behavior track on a front-end interface according to the limiting condition. The client can understand the recommendation basis of the recommendation algorithm through the content, can verify the effective condition of the operation strategy, and can discover the hidden association relationship so as to supplement the characteristics of the algorithm or perfect the operation strategy.

Description

technical field [0001] The present invention relates to the technical field of data analysis, in particular to a method, system, electronic device and readable storage medium for explaining recommendation effects based on user behavior traces. Background technique [0002] With the rapid development of the network, the rapid expansion of information, and the accelerated pace of user behavior, it is increasingly necessary to quickly obtain information about the direct or potential needs of users. Under this demand, the application of recommendation systems is becoming more and more extensive. [0003] The current recommendation effect is to push the recommendation results through the model or operation strategy or a combination of the two. Subsequent optimization is mainly to update and iterate the model or add more features through accumulated data. Customers often check the effect Through basic indicators such as exposure, clicks, and conversion rates, in order to meet cust...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06F16/955
CPCG06F16/9535G06F16/9562
Inventor 黄山姗
Owner BEIJING MININGLAMP SOFTWARE SYST CO LTD
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