Content recommendation method, device and equipment and readable storage medium
A content recommendation and content technology, which is applied in the direction of instruments, semantic analysis, computer components, etc., can solve the problems that keyword tags cannot understand the semantics of articles, and the accuracy of content recommendation is low, so as to improve the accuracy of analysis and recommendation Effect
Pending Publication Date: 2020-10-27
TENCENT TECH (BEIJING) CO LTD
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[0004] Then, when recommending content through the above method, because the semantics of the article cannot be understood through the form of keyword tags, the same tag expresses different meanings in different content, but the same code will be generated, resulting in a lower accuracy rate of content recommendation. Low
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[0030] In order to make the purpose, technical solution and advantages of the present application clearer, the implementation manners of the present application will be further described in detail below in conjunction with the accompanying drawings.
[0031] First, a brief introduction to the nouns involved in the embodiments of this application:
[0032] Artificial Intelligence (AI): It is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the nature of intelligence and produce a new kind of intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods ...
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The invention discloses a content recommendation method, device and equipment and a readable storage medium, and relates to the field of content recommendations. The method comprises the steps of obtaining historical interaction content of a content recommendation pool and a target account in a historical time period, wherein the historical interaction content corresponds to a first title, and thecontent recommendation pool comprises a second title; inputting the first title and the second titles into a recall model, and outputting to obtain n second titles associated with the first title, and wherein n is a positive integer; and according to the n first candidate contents corresponding to the n second titles, determining target contents recommended to the target account. Similarity analysis is carried out on titles of the content through a recall model; therefore, the candidate content is determined from the content recommendation pool according to the historical content, the targetcontent is recommended to the target account according to the candidate content, and the title similarity is determined through the high-level semantic implicit vector, so that the analysis accuracy of the similarity is improved, and the recommendation accuracy of the content is further improved.
Description
technical field [0001] The embodiments of the present application relate to the field of content recommendation, and in particular to a content recommendation method, device, device, and readable storage medium. Background technique [0002] The recommendation system is used in the field of recommending content such as videos, news, and e-commerce, and is used to recommend content that users may be interested in. Schematically, if the user has watched article A, article B related to article A is recommended to the user according to article A. [0003] In related technologies, when recommending content, content is recommended to the user according to the keyword tag corresponding to the content. For example, if the keyword tag identified by article A is "falling", then when the user clicks on article A, the When recommending content to users, recommend content marked with the tag "drop". [0004] Then, when recommending content through the above method, because the semantic...
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IPC IPC(8): G06F16/9536G06F40/30G06F40/258G06K9/62
CPCG06F16/9536G06F40/30G06F40/258G06F18/22
Inventor 张新宇
Owner TENCENT TECH (BEIJING) CO LTD