Model generation method and network information pushing method
A network information and model generation technology, which is applied in the fields of intelligent search, deep learning and big data, can solve the problems of single application scenarios of push models and inability to meet the diversification and flexibility of network information push
Active Publication Date: 2021-11-16
BEIJING BAIDU NETCOM SCI & TECH CO LTD
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[0004] However, using the above method, the generated push model has a single application scenario, which cannot meet the diversification and flexibility of network information push.
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[0043] Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and they should be regarded as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
[0044] Network information refers to information released on the Internet using network technology, including news, information, videos, virtual items, etc.
[0045] With the increase in the quantity and category of network information, users relatively need to spend more time and effort to find and browse the network information that meets their needs from the massive ...
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The invention provides a model generation method and a network information pushing method, and relates to the technical field of intelligent search, deep learning and big data in the technical field of artificial intelligence. The method comprises the steps of inputting features of acquired sample network information to each neural network to obtain high-order features output by each neural network, inputting the high-order features to each multi-head attention network for the high-order features output by each neural network to obtain output features outputted by each multi-head attention network, and outputting the output features to each multi-head attention network to obtain the output features of each multi-head attention network, for the output features outputted by each multi-head attention network, transmitting the output features outputted by the multi-head attention network to a task network corresponding to the multi-head attention network to obtain a loss value of the task network, and adjusting parameters of each neural network and parameters of each multi-head attention network are adjusted based on the loss value of each task network, so that a pushing model for pushing network information is obtained, and the flexibility and diversity of network information pushing are improved.
Description
technical field [0001] The present disclosure relates to the field of intelligent search, deep learning and big data technology in the field of artificial intelligence technology, and in particular to a method for generating a model and a method for pushing network information. Background technique [0002] With the development of artificial intelligence technology and Internet technology, the amount of network information has increased dramatically, and the push model of network information has emerged based on the user's demand for network information. Wherein, the network information includes news, information, video and so on. [0003] In related technologies, the commonly used method for generating a push model is: based on different push needs of users, a push model corresponding to each push demand is generated. For example, when some users watch a video, they want to push The video is a video with a relatively high click-through rate, that is, the user's push demand...
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IPC IPC(8): G06F16/9535G06N3/04G06N3/08
CPCG06F16/9535G06N3/08G06N3/045Y02D10/00
Inventor 葛利
Owner BEIJING BAIDU NETCOM SCI & TECH CO LTD