Video recommendation method, device and computer-readable storage medium
A video recommendation and video technology, applied in video data query, video data retrieval, biological neural network models, etc., can solve the problems of reducing the accuracy of video recommendation and insufficient triggering of personalized videos, and achieve the effect of improving accuracy
Active Publication Date: 2021-10-26
BEIJING DAJIA INTERNET INFORMATION TECH CO LTD
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Problems solved by technology
This method cannot use user attributes to recommend related videos. When a user is a new user or the user has few historical behaviors, it will cause insufficient triggering of personalized videos and reduce the accuracy of video recommendation.
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[0115] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present application as recited in the appended claims.
[0116] figure 1 It is a flowchart of a video recommendation method shown according to an exemplary embodiment, specifically including the following steps:
[0117] In step S101, a click rate prediction model is established based on a neural network algorithm.
[0118] In this step, a click-through rate prediction model is established based on a neural network algorithm. The click rate...
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The present application relates to a video recommendation method, device and computer storage medium. The video recommendation method includes: establishing a click-through rate estimation model based on a neural network algorithm; calculating the similarity between the attribute characteristics of the target user and the attribute characteristics of multiple historical users respectively, and obtaining the attribute characteristics closest to the target user the first historical user; based on the click-through rate estimation model, obtain the click-through rate of the first historical user on the related video; and based on the click-through rate of the first historical user on the related video, the related Multiple videos in the video are recommended to the target user. In this video recommendation method, the user's attribute characteristics are fully used to estimate the user's click rate on related videos, thereby improving the accuracy of estimating the click rate of new users or users with less user behavior on related videos, thereby improving It improves the accuracy of video recommendation to new users or users with less user behavior.
Description
technical field [0001] The present application belongs to the field of computer software applications, in particular to a video recommendation method and device. Background technique [0002] With the advancement of science and technology and the popularization of the Internet, more and more people transmit information and share life through video, and massive personalized video recommendation becomes increasingly important. At present, the most widely used method is to use machine learning methods to predict the user's click rate on videos and other goals. [0003] In the personalized video recommendation, it is usually divided into two stages: the first stage is video triggering, which finds out the video collection that the user may be interested in from the video collection according to the user's attributes and behaviors. The second stage is video sorting. The neural network model or strategy is used to score the video sorting triggered in the first stage, and find out...
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IPC IPC(8): G06F16/73G06N3/04
Inventor 蔡锦龙
Owner BEIJING DAJIA INTERNET INFORMATION TECH CO LTD