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Content recommendation method and device, electronic equipment and storage medium

A content recommendation and content technology, applied in the field of deep learning, can solve problems such as poor robustness and the inability of recommendation systems to meet the recommendation needs of small sample users.

Pending Publication Date: 2022-06-17
BEIJING DAJIA INTERNET INFORMATION TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In this way, the recommendation system will not be able to meet the recommendation needs of small sample users, and the robustness will be poor.

Method used

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  • Content recommendation method and device, electronic equipment and storage medium
  • Content recommendation method and device, electronic equipment and storage medium
  • Content recommendation method and device, electronic equipment and storage medium

Examples

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Embodiment Construction

[0053] In order to make those skilled in the art better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0054] It should be noted that the terms "first", "second" and the like in the description and claims of the present disclosure and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used may be interchanged under appropriate circumstances such that the embodiments of the disclosure described herein can be practiced in sequences other than those illustrated or described herein. The implementations described in the illustrative examples below are not intended to represent all implementations consistent with this disclosure. Rather, they are merely examples of apparatus and methods ...

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Abstract

The invention relates to a content recommendation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining content features of candidate content, user account features of a user account and a first prediction result corresponding to the user account features, and the first prediction result comprises a first prediction probability that the user account executes each preset operation on the candidate content; determining a target cluster transformation network most matched with the user account feature from a plurality of pre-trained cluster transformation networks, and extracting a target intermediate feature from the first prediction result by using the target cluster transformation network; and inputting the target intermediate feature and the content feature into a pre-trained prediction network, the prediction network being used for outputting a second prediction result, and determining recommended content corresponding to the user account from the candidate content according to the second prediction result. According to the method, part of parameters in the prediction model can meet the recommendation requirements of small sample users, and the robustness of the prediction model is effectively improved.

Description

technical field [0001] The present disclosure relates to the technical field of deep learning, and in particular, to a content recommendation method, apparatus, electronic device, and storage medium. Background technique [0002] At present, the recommendation system completes the content recommendation task by sequentially performing recall, rough sorting, fine sorting, sorting, rearranging and other links. Usually, in each link, the probability of the user account performing a certain operation on the content is predicted based on the content and the behavior habits of the user account, such as the probability of performing a like operation and a comment operation on the content, and then performing a certain operation on the content according to the user account. The probability of each action recommends content to the user account. Wherein, the aforementioned content may be music, short videos, pictures and texts, and the like. [0003] In the related art, the recommen...

Claims

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Application Information

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IPC IPC(8): G06F16/9536G06F16/9535G06N3/04G06N3/08
CPCG06F16/9536G06F16/9535G06N3/08G06N3/045
Inventor 王晓伟杨森
Owner BEIJING DAJIA INTERNET INFORMATION TECH CO LTD
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