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Object matching method, model training method, product matching method and storage medium

A matching model and object matching technology, which is applied in the field of semantic recognition, can solve the problems of low semantic matching accuracy and achieve the effects of solving low recognition accuracy, improving representation ability, and improving anti-interference ability

Pending Publication Date: 2022-08-05
ALIBABA (CHINA) 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 an object matching method, a model training method, a product matching method and a storage medium, so as to at least solve the technical problem of low semantic matching accuracy in related technologies

Method used

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  • Object matching method, model training method, product matching method and storage medium
  • Object matching method, model training method, product matching method and storage medium
  • Object matching method, model training method, product matching method and storage medium

Examples

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Embodiment 1

[0041] According to the embodiments of the present application, an object matching method, or a product matching method, or an embodiment of a model training method is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a set of computers, such as The instructions are executed in a computer system, and, although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that herein.

[0042] The method embodiment provided in the first embodiment of the present application may be executed in a mobile terminal, a computer terminal, a server, a cloud server, or a similar computing device. figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing an object matching method, a product matching method, or a model training method is shown. like figure 1 As shown, the computer terminal 10 (or mobile dev...

Embodiment 2

[0102] This application provides such as Figure 5 The object matching method shown, Figure 5 It is a flowchart of the object matching method according to Embodiment 2 of the present application, comprising the following steps:

[0103] Step S502, the cloud server receives the object search request sent by the client, wherein the object search request is generated by superimposing multiple search keywords.

[0104] Step S504, the cloud server obtains the description information of the target object based on the object search request.

[0105] Step S506, the cloud server uses the semantic matching model to perform semantic matching on multiple retrieval keywords and description information, and obtains the target matching result between the object search request and the target object, wherein the semantic matching model passes the first matching result of the training sample and the confrontation sample. The second matching result is obtained by training the pre-training mod...

Embodiment 3

[0113] This application provides such as Image 6 The object matching method shown, Image 6 It is a flow chart of the product matching method according to the embodiment 3 of the present application, comprising the following steps:

[0114] Step S602: Obtain the product search request and title information of the target product, wherein the product search request is generated by superimposing multiple search keywords.

[0115] Step S604, using the semantic matching model to perform semantic matching on a plurality of retrieval keywords and title information to obtain the target matching result between the product search request and the target product, wherein the semantic matching model passes the first matching result of the training sample and the first matching result of the confrontation sample. The second matching result is obtained by training the pre-training model, the adversarial sample is generated by superimposing noise data on the training sample, the first match...

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Abstract

The invention discloses an object matching method, a model training method, a product matching method and a storage medium. The method comprises the steps of obtaining an object search request and description information of a target object; and performing semantic matching on the plurality of retrieval keywords and the description information by utilizing a semantic matching model to obtain a target matching result of the object search request and the target object. According to the method and the device, the anti-interference capability of the model on the redundant information of the keyword stacked text is improved by means of adversarial training in the training process of the model, and moreover, the distinguishing of the contrast learning enhancement model on positive and negative samples is fused in the adversarial training process, so that the characterization capability of the object search request and the description information of the target object is improved; therefore, the recognition capability of the semantic matching model on the fine semantics can be improved, the robustness is improved, the technical effect of improving the model recognition accuracy is further achieved, and the technical problem that in the prior art, the recognition accuracy of the semantic matching model is low is solved.

Description

technical field [0001] The present application relates to the field of semantic recognition, and in particular, to an object matching method, a model training method, a product matching method and a storage medium. Background technique [0002] As an important part of the search field, semantic matching can make each user's search result related to the search text input by the user. In order to achieve a better user search experience effect, Transformer-based (Transformer is an attention mechanism-based model) is usually used. pretrained model for semantic matching. [0003] However, Transformer-based pre-training models are usually trained on large-scale general expectations, and cannot be well adapted to scenarios that consider subtle semantic differences. For example, in an online shopping scenario, the search text entered by the user is "300ml fitness In the case of "water cups", the search results will arrange water cups of various volumes in the front and display them...

Claims

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Application Information

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IPC IPC(8): G06F16/33G06F16/36G06F40/166G06F40/295G06N3/08
CPCG06F16/3344G06F16/36G06N3/08G06F40/166G06F40/295
Inventor 陈犇金林波蒋文
Owner ALIBABA (CHINA) CO LTD
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