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Model evaluation method and device, equipment and medium

A technology of models and evaluation indicators, applied in the computer field, can solve problems such as non-targeted and large evaluation granularity, and achieve the effects of wide applicability, improved accuracy, and improved stability

Pending Publication Date: 2020-07-28
BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the large evaluation granularity, the model evaluation results are not targeted for different users

Method used

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  • Model evaluation method and device, equipment and medium
  • Model evaluation method and device, equipment and medium
  • Model evaluation method and device, equipment and medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] figure 1 It is a flowchart of a model evaluation method disclosed in Embodiment 1 of the present application. This embodiment can be applied to the situation of performing targeted and individualized evaluation on recommendation models, and the recommendation models include binary classification recommendation models and multi-classification recommendation models. The method of this embodiment can be executed by a model evaluation device, which can be implemented by software and / or hardware, and can be integrated on any electronic device with computing capabilities, such as a server.

[0047] Such as figure 1 As shown, the model evaluation method disclosed in this embodiment may include:

[0048] S101. Obtain behavior data of the target user with respect to recommendation results within a preset interaction period, wherein the recommendation results are determined using a recommendation model.

[0049] Wherein, the target user may refer to a specific user or a type of...

Embodiment 2

[0057] figure 2 It is a flow chart of a model evaluation method disclosed in Embodiment 2 of the present application, which is further optimized and expanded based on the above embodiments, and can be combined with the above optional implementation modes. Such as figure 2 As shown, the method may include:

[0058] S201. Obtain the target user's behavior data on recommendation results within multiple preset interaction periods, wherein the recommendation results are determined using a recommendation model.

[0059] The stability of model evaluation results can be improved by statistically analyzing the target user's behavior data on recommendation results in multiple (referring to at least two) preset interaction periods for use in evaluation of recommendation models. The length of time corresponding to the multiple preset interaction periods may be in units of hours, days or months.

[0060] S202. Using the user behavior data in each preset interaction period, feedback an...

Embodiment 3

[0074] image 3 It is a schematic structural diagram of a model evaluation device disclosed in Embodiment 3 of the present application. This embodiment can be applied to the situation of performing targeted and personalized evaluation on recommended models. The apparatus in this embodiment can be implemented by software and / or hardware, and can be integrated on any electronic device with computing capabilities, such as a server.

[0075] Such as image 3 As shown, the model evaluation device 300 disclosed in this embodiment may include a behavior data acquisition module 301, a recommendation result labeling module 302, and a model evaluation module 303, wherein:

[0076] A behavioral data acquisition module 301, configured to acquire the target user's behavioral data for the recommendation result within a preset interaction period, wherein the recommendation result is determined using a recommendation model;

[0077] The recommendation result labeling module 302 is used to u...

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Abstract

The embodiment of the invention discloses a model evaluation method and device, equipment and a medium, and relates to the machine learning technology, and the method comprises the steps: obtaining behavior data of a target user for a recommendation result in a preset interaction period, wherein the recommendation result is determined through a recommendation model; performing feedback labelingon a recommendation result in a preset interaction period by utilizing the behavior data; and evaluating the recommendation model according to the labeling result of the recommendation result in the preset interaction period. According to the embodiment of the invention, personalized model evaluation effects for different users can be realized, and the accuracy of a model evaluation result is improved with more refined time granularity.

Description

technical field [0001] The embodiments of the present application relate to computer technology, in particular to machine learning technology, and in particular to a model evaluation method, device, device and medium. Background technique [0002] In e-commerce or advertising scenarios, the online effect of the ranking model directly affects the user's satisfaction with the recommendation results. At present, regarding the recommendation model, the model is only evaluated from the overall performance. Due to the large evaluation granularity, the model evaluation results are not targeted for different users. Contents of the invention [0003] The embodiment of the present application discloses a model evaluation method, device, device, and medium, so as to realize personalized model evaluation effects for different users, and improve the accuracy of model evaluation results with finer time granularity. [0004] In the first aspect, the embodiment of the present applicatio...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06K9/62G06Q10/06
CPCG06F16/9535G06Q10/06393G06F18/214
Inventor 刘涛
Owner BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD
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