Sequence recommendation model training and application method and device, equipment and storage medium

A training method and sequence technology, applied in the field of deep learning, can solve problems such as poor model performance, and achieve the effect of increasing capture and improving recommendation performance

Active Publication Date: 2021-11-02
PING AN TECH (SHENZHEN) CO LTD
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The current BERT4Rec model only captures the characteristics of part of the product shopping sequence, ignoring the correlation between the user vector representing user characteristics and the entire historical shopping behavior. Therefore, when the shopping sequence is too short or there is interference from users’ unconscious clicks, the performance of the model is relatively poor. Difference

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  • Sequence recommendation model training and application method and device, equipment and storage medium
  • Sequence recommendation model training and application method and device, equipment and storage medium
  • Sequence recommendation model training and application method and device, equipment and storage medium

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Embodiment Construction

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0036] The flow charts shown in the drawings are just illustrations, and do not necessarily include all contents and operations / steps, nor must they be performed in the order described. For example, some operations / steps can be decomposed, combined or partly combined, so the actual order of execution may be changed according to the actual situation.

[0037] It should be understood that the terms used in the specification of this application are for the purpose of ...

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Abstract

The invention relates to the technical field of artificial intelligence, and provides a sequence recommendation model training and application method and device, equipment and a storage medium. The method comprises the following steps: acquiring a user identifier and corresponding historical purchased commodity information as a first sample; adding a random mask to the commodity information in the first sample to obtain a second sample; taking the plurality of pieces of commodity information in the first sample as positive samples, taking the plurality of pieces of commodity information in the commodity corpus as negative samples, and inputting a third sample formed by the first sample and the positive and negative samples into the model to obtain a user vector and a positive and negative sample commodity vector; determining a first loss value according to positive and negative matching degrees respectively determined by the user vector and the positive and negative sample commodity vectors; inputting the second sample into the model, outputting a predicted value of the randomly masked commodity information, and determining a second loss value according to the predicted value and a true value of the randomly masked commodity information; and optimizing parameters of the model according to the first loss value and the second loss value to obtain a trained sequence recommendation model.

Description

technical field [0001] The present application relates to the technical field of deep learning, and in particular to a training method, application method, device, computer equipment and storage medium of a sequence recommendation model. Background technique [0002] There have been many studies in the field of intelligent recommendation. Among them, the sequence recommendation method based on deep learning is to recommend the first preset quantity according to the user's historical shopping sequence. The mainstream research direction is to introduce Transformer with attention mechanism into sequence recommendation. Among them, BERT4Rec is based on BERT structure, introduces a bidirectional Transformer layer, and introduces a mask mechanism to train the prediction level of the model. The current BERT4Rec model only captures the characteristics of some product shopping sequences, ignoring the correlation between the user vector representing user characteristics and the entire...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/06G06F16/35G06N3/04
CPCG06Q30/0631G06F16/35G06N3/045
Inventor 颜泽龙王健宗
Owner PING AN TECH (SHENZHEN) CO LTD
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