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Recommendation method and system based on unsupervised learning

An unsupervised learning and recommendation method technology, applied in neural learning methods, special data processing applications, instruments, etc., can solve problems such as a lot of manpower, reduce resources and alleviate insufficient labeling resources.

Pending Publication Date: 2022-01-07
新华智云科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to provide a recommendation method and system based on unsupervised learning to solve the technical problem of requiring a large amount of manpower to label resources in the recommendation method existing in the above-mentioned prior art

Method used

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  • Recommendation method and system based on unsupervised learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] Such as figure 1 As shown, the present embodiment includes a recommendation method based on unsupervised learning, including the following steps: acquiring first training data, inputting the first training data into the bert model for the first time, obtaining second training data, and second training data of the first training data Enter the bert model for the second time to obtain the third training data;

[0047] The second training data and the third training data form a positive sample pair and a negative sample pair, and the positive sample pair and negative sample are input into the loss function to obtain the loss value; judge whether the loss value is within the preset threshold, if not, repeat the operation to obtain the first Training data, the first training data is input to the bert model for the first time and the subsequent steps; if so, an accurate bert model is obtained;

[0048] Obtain user data, input the processed user data into the precise bert mod...

Embodiment 2

[0059] This embodiment includes a recommendation system based on unsupervised learning, including:

[0060] The first training module is used to obtain the first training data, and the first training data is input into the bert model for the first time to obtain the second training data, and the first training data is input to the bert model for the second time to obtain the third training data;

[0061] The second training module is used for the second training data and the third training data to form a positive sample pair and a negative sample pair, and the positive sample pair and the negative sample are input into a loss function to obtain a loss value.

[0062] The judging module is used to judge whether the loss value is within the preset threshold, if not, repeat the operation to obtain the first training data, the first training data is input into the bert model for the first time and the subsequent steps; if so, an accurate bert model is obtained;

[0063] The recomm...

Embodiment 3

[0066] A computer-readable storage medium. Computer instructions are stored on the computer-readable storage medium. When the computer instructions are executed by a processor, the steps of the method in Embodiment 1 are implemented.

[0067] Those skilled in the art should understand that the embodiments of the present invention may be provided as methods, apparatuses, or computer program products. Accordingly, the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

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Abstract

The invention discloses a recommendation method and system based on unsupervised learning. The method comprises the following steps: obtaining first training data, inputting the first training data into a bert model for the first time to obtain second training data, and inputting the first training data into the bert model for the second time to obtain third training data; forming a positive sample pair and a negative sample pair by the second training data and the third training data, and inputting the positive sample pair and the negative sample pair into a loss function to obtain a loss value; judging whether the loss value is within a preset threshold value or not, and if yes, obtaining an accurate bert model; and obtaining user data, inputting the processed user data into a precise bert model, calculating a similarity score of the user data and to-be-recommended data, and recommending the user according to the similarity score. The invention has the beneficial effects that the problems of multi-layer feature representation and one-word polysemy of words can be better solved, resources needing manual annotation are reduced, the problem of insufficient annotation resources can be well relieved, and meanwhile the recommendation speed can be greatly increased.

Description

technical field [0001] The invention belongs to the technical field of recommendation methods, and more specifically, the invention relates to a recommendation method and system based on unsupervised learning. Background technique [0002] In recommendation system projects, such as the recommendation of film and television works, text similarity is usually used to recommend similar items. Text similarity is a commonly used recommendation algorithm. The application of text similarity in recommender system projects can be attributed to calculating the similarity score for the profile text, sorting by the similarity score, and recommending according to the sorting results. In the recommendation projects of industrial scenarios, we often face the problem of unlabeled data, and we also need to consider the performance of the recommendation algorithm. The current technical solutions for the recommendation projects of industrial scenarios have the following defects: [0003] First...

Claims

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Application Information

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IPC IPC(8): G06F16/9035G06K9/62G06N3/08
CPCG06F16/9035G06N3/088G06F18/22G06F18/214
Inventor 郭鑫润
Owner 新华智云科技有限公司
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