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Recommended content determination method and device and storage medium

A technology for recommending content and determining methods, applied in biological neural network models, marketing, advertising, etc., can solve problems such as inability to personalize recommendations for new content and new users, modeling of new content and new users

Inactive Publication Date: 2021-05-07
SENSOR NETWORKS TECH BEIJING CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Embodiments of the present invention provide a method, device, and storage medium for determining recommended content, to at least solve the problem that the recommendation system in the related art cannot accurately model new content and new users Technical issues with personalized recommendations

Method used

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  • Recommended content determination method and device and storage medium
  • Recommended content determination method and device and storage medium
  • Recommended content determination method and device and storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0025] According to an embodiment of the present invention, a method for determining recommended content is provided, such as figure 1 As shown, the method includes:

[0026] S102. Determine the predicted click probabilities corresponding to the plurality of contents to be recommended corresponding to the user according to the prediction neural network model, wherein the prediction neural network model is obtained by training according to the user characteristics of the user, the user's historical browsing content, and the content to be recommended;

[0027] S104, sort the plurality of content to be recommended according to the predicted click probability, so as to determine the target recommended content.

[0028] In related technologies, generally there are multiple contents to be recommended for a user, which are located in a content library to be recommended, and are ready to be recommended for the user. For example, the content displayed for the user when the user logs i...

Embodiment 2

[0049] According to an embodiment of the present invention, there is also provided a device for determining recommended content for implementing the above method for determining recommended content, such as Figure 4 As shown, the device includes:

[0050] 1) The determination unit 40 is configured to determine the predicted click probabilities corresponding to the multiple contents to be recommended corresponding to the user according to the predicted neural network model, wherein the predicted neural network model is based on the user characteristics of the user, the user's The historical browsing content and the content to be recommended are trained;

[0051] 2) The processing unit 42 is configured to sort the plurality of content to be recommended according to the predicted click probability, so as to determine the target recommended content.

[0052] Optionally, in this embodiment, it also includes:

[0053] 1) The first acquisition unit is used to acquire the user char...

Embodiment 3

[0059] The embodiment of the invention also provides a storage medium. Optionally, a storage medium, the storage medium includes a stored program, wherein the program executes the method for determining recommended content as described above when running.

[0060] Optionally, in this embodiment, the storage medium is configured to store program codes for performing the following steps:

[0061] S1. Determine the predicted click probabilities corresponding to the plurality of content to be recommended corresponding to the user according to the prediction neural network model, wherein the prediction neural network model is based on the user characteristics of the user, the user's historical browsing content and the content to be recommended obtained by content training;

[0062] S2 sorts the plurality of content to be recommended according to the predicted click probability, so as to determine the target recommended content.

[0063] Optionally, in this embodiment, the above-m...

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Abstract

The embodiment of the invention relates to a recommendation content determination method and device and a storage medium. The method comprises the steps of determining predicted click probabilities corresponding to multiple to-be-recommended contents corresponding to a user according to a predicted neural network model, and sorting the multiple to-be-recommended contents according to the predicted click probabilities to determine target recommended contents. According to the invention, the technical problem that accurate personalized recommendation cannot be carried out for new contents and new users due to the fact that a recommendation system in the related technology cannot carry out modeling on the new contents and the new users is solved.

Description

technical field [0001] The present invention relates to the field of advertisement delivery, in particular to a method, device and storage medium for determining recommended content. Background technique [0002] With the research and development of deep learning technology, the application of deep learning to recommendation systems is also known as a research hotspot. More and more recommendation systems have begun to adopt deep learning technology, and have achieved significant performance improvements. The solutions represented by (DeepNeural Networks for YouTube Recommendations) and (DSSM) use user behavior data and apply deep learning to the recommendation system, which has achieved significant improvement. [0003] In this type of algorithm based on user behavior, there is a cold start problem of new content and new users. This problem is due to the lack of user behavior of new content and new users, and it is difficult for deep learning algorithms to model them, so ...

Claims

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Application Information

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IPC IPC(8): G06F16/9535G06N3/04G06Q30/02
CPCG06F16/9535G06Q30/0255G06Q30/0271G06N3/047G06N3/045
Inventor 桑文锋曹犟刘耀洲付力力胡士文
Owner SENSOR NETWORKS TECH BEIJING CO LTD
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