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Method, device and system for deep learning

A deep learning and scheduling platform technology, applied in the field of deep learning, can solve problems such as poor matching between recommended products and user interests, and achieve the effect of accurate in-depth prediction

Active Publication Date: 2021-04-06
阿里巴巴(中国)网络技术有限公司
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  • Claims
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Problems solved by technology

[0006] The embodiment of the present invention provides a deep learning method, device and system, which can solve the problem of poor matching between recommended products and user interests

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  • Method, device and system for deep learning
  • Method, device and system for deep learning
  • Method, device and system for deep learning

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Embodiment Construction

[0064] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0065] Embodiments of the present invention provide a deep learning system, such as figure 1 As shown, the system includes: computing nodes 11 and scheduling platform 12, wherein:

[0066] The scheduling platform 12 is configured to send the deep learning model and prediction samples to the computing nodes 11.

[0067] The scheduling platform 12 assigns forecast tasks to computing nodes 11 for calculation according to the actual f...

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Abstract

The invention discloses a deep learning method, device and system, relates to the technical field of the Internet, and can solve the problem of poor matching between recommended commodities and user interests. The method of the present invention includes: the scheduling platform sends the deep learning model and the prediction sample to the computing node; the computing node extracts the sample feature in the form of a one-dimensional vector from the forecasting sample, and converts the sample feature in the form of a one-dimensional vector into a sample in the form of a two-dimensional array Features, based on the deep learning model, generalize the sample features in the two-dimensional array in units of two-dimensional sub-arrays, and obtain the processing results; the dispatching platform receives the processing results sent by the computing nodes, and outputs the learning results according to the processing results. The invention is mainly applied in the process of recommending commodities to users by shopping websites.

Description

technical field [0001] The present invention relates to the field of Internet technologies, in particular to a deep learning method, device and system. Background technique [0002] The product recommendation function is an information exposure method often used by shopping websites. This function can recommend other products of the same category or used in combination to the user based on the operation records of a single user's browsing, collection or purchase of products. Collect or purchase statistical data to recommend popular online products to users. From the user's point of view, the product recommendation function can provide richer product information, which is convenient for users to compare different products horizontally and make rational purchase decisions; from the perspective of shopping websites, the product recommendation function can increase the traffic guidance of product pages , Improve the conversion rate of commodity transactions. Due to its many ad...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/06G06N3/04G06N3/08
Inventor 张斌刘忠义
Owner 阿里巴巴(中国)网络技术有限公司
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