News personalized intelligent recommendation method and system based on deep learning
A technology of deep learning and recommendation methods, applied in the direction of neural learning methods, instruments, biological neural network models, etc., can solve problems such as wrong recommendation of historical behavior data, bad user experience, etc., and achieve the effect of improving experience
Active Publication Date: 2020-11-27
吉浦斯信息咨询(深圳)有限公司
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Problems solved by technology
[0003] However, in related technologies, when the user's historical behavior is missing, one-sided or unreasonable application of historical behavior data will lead to wrong recommendations and bring bad experience to users
For example, when encountering new users and users who have not collected historical behavior data, they usually recommend news randomly; for example, when only part of the news that the user is interested in is collected, it usually results in only recommending a single type of news to the user. news
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[0023] The present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings. Wherein, similar elements in different implementations adopt associated similar element numbers. In the following implementation manners, many details are described for better understanding of the present application. However, those skilled in the art can readily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, and methods. In some cases, some operations related to the application are not shown or described in the description, this is to avoid the core part of the application being overwhelmed by too many descriptions, and for those skilled in the art, it is necessary to describe these operations in detail Relevant operations are not necessary, and they can fully understand the relevant operations according to the description in the specification and genera...
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The invention provides a personalized intelligent news recommendation method and system based on deep learning. The method includes the following steps that user mobile-terminal Internet behavior datais obtained, and according to the BP neural network, the user's real-time interest and hobby labels are predicted; according to the user's real-time interest and hobby labels, news corresponding to atheme and the interest and hobby labels is recommended to a user; the reading situation of the user is obtained; according to the reading situation of the user, the user's real-time interest and hobby labels are amended, next news recommendation is conducted, according to the user's real-time interest and hobby labels, news recommendation thus can be conducted, a new user also can obtain the newswhich the new user is interested in, meanwhile according to the reading situation of the user, the user's real-time interest and hobby labels can be corrected timely, the accuracy of the labels is ensured, meanwhile the types of the labels are enriched, the situation that recommended news themes is less and less can be avoided, the convergence and divergence of the system are also recommended, and the user experience is improved.
Description
technical field [0001] The present invention relates to Internet news push, in particular to a deep learning-based news personalized intelligent recommendation method, a computer-readable storage medium, a deep learning-based news personalized intelligent recommendation system, and a deep learning-based news Personalized intelligent recommendation device. Background technique [0002] With the rapid development of the Internet, the number of various news is increasing by hundreds of millions every day, making it difficult for users to browse the news they are interested in in a timely manner, which brings users a bad experience. In order to solve this problem, usually The news that users are interested in is recommended to them in a timely manner through a personalized news recommendation system. [0003] However, in related technologies, when the user's historical behavior data is missing, one-sided or unreasonable application of historical behavior data will lead to wrong...
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IPC IPC(8): G06F16/9535G06N3/04G06N3/08
CPCG06F16/9535G06N3/084
Inventor 余承乐洪晶陈宇
Owner 吉浦斯信息咨询(深圳)有限公司