Content ordering method, computing device, and computer-readable storage medium
A content and sequence technology, applied in computing equipment and computer-readable storage media, and in the field of content sorting methods, can solve problems such as difficulties in learning user interests
Active Publication Date: 2022-04-08
上海任意门科技有限公司
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Problems solved by technology
Through these methods, the solution of the present invention solves the problem of difficulty in learning user interests on a large-traffic social platform in a decentralized scenario, thereby providing users with more accurate content recommendations
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[0027] Preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although preferred embodiments of the invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
[0028] In the following description, for the purposes of illustrating various inventive embodiments, certain specific details are set forth in order to provide a thorough understanding of various inventive embodiments. One skilled in the relevant art will recognize, however, that an embodiment may be practiced without one or more of these specific details. In other instances, well-known devices, structures and techniques associated with the present application...
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The present disclosure provides a content sorting method, a computing device, and a computer-readable storage medium. The method includes: determining a user's training sample based on the flow log of the social platform, where the flow log records the user's behavior on multiple contents of the social platform, and the training sample includes a first user dense vector, a first content dense vector and The first user behavior sequence vector; based on the training sample, the deep neural network model is trained to generate a training target vector, the training target vector includes a second user dense vector, a second content dense vector as an interest feature vector, and a second user behavior sequence vector , and the training target vector indicates the result of at least one user behavior for each content; the weight function of the deep neural network model is updated based on the training target vector; and a plurality of candidate candidates are respectively calculated based on the trained deep neural network model content to rank multiple content candidates.
Description
technical field [0001] The present invention generally relates to the field of machine learning, and more specifically, relates to a content sorting method, a computing device, and a computer-readable storage medium. Background technique [0002] With the continuous development of various social platforms, more and more users obtain various information through these social platforms. Usually, when a user logs in to a social platform, the server of the social platform recommends some content to the user and displays it on the user terminal. However, a large amount of content is generated on social platforms all the time, and the amount of content that can be displayed by user terminals is limited, and if several items in the head cannot arouse the user's interest, the user is likely to turn to other forums or other platforms. Therefore, how to display the content that best matches the user's interests in a limited head position is an important means to improve the content re...
Claims
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IPC IPC(8): G06F16/9535G06F16/9536G06F16/901G06F16/908G06N3/04G06N3/08
CPCG06F16/9535G06F16/9536G06F16/9014G06F16/908G06N3/084G06N3/048
Inventor 黄清纬彭飞唐文斌
Owner 上海任意门科技有限公司