A Short Video Click Rate Prediction Method Based on Sequential Capsule Network
A prediction method and short video technology, applied in the field of Internet services, can solve the problems of loss of information, masking users' secondary interests, and not considering the variety of user interests, and achieve the effect of accurate prediction.
Active Publication Date: 2021-11-23
CHINA JILIANG UNIV
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Problems solved by technology
[0004] The above methods all take into account the sequence of the user's short video click sequence, but they do not take into account the diverse interests of the user
When the above methods model user interests, they directly treat user interests as a whole, which will lose a lot of information, especially the user's main interest will cover up the user's secondary interest
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[0032] In order to further understand the present invention, a method for predicting the click-through rate of a short video based on a sequence capsule network provided by the present invention will be specifically described below in conjunction with specific embodiments, but the present invention is not limited thereto. The non-essential improvements and adjustments made below still belong to the protection scope of the present invention.
[0033] The short video click rate prediction task is to build a model to predict the probability of users clicking on short videos. The user's click sequence is expressed as x j Represents the jth short video, n is the length of the sequence. Therefore, the short video click rate prediction problem can be expressed as: input user click sequence u and target short video x new , to predict the user’s response to the target short video x new click-through rate.
[0034] For this reason, the present invention proposes a short video clic...
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The invention discloses a short video click rate prediction method based on a sequence capsule network. Based on the user's click sequence on the short video, the method uses the sequence capsule network to mine the user's multiple interests and predict the user's click rate on the target short video. This method is mainly composed of three parts: the first part is to use the convolutional neural network to extract contextual features from the user click sequence; the second part is to use the sequence capsule network to convert the contextual features to different interest spaces, and in different interest spaces The sequence of user behavior is captured to obtain the user's multi-interest vector representation; the third part is based on the user's multi-interest vector representation to predict the click-through rate of short videos.
Description
technical field [0001] The invention belongs to the technical field of Internet services, and in particular relates to a method for predicting the click-through rate of short videos based on sequence capsule networks. Background technique [0002] Short video is a new type of short video. The shooting of short videos does not require the use of professional equipment or professional skills. Users can easily shoot and upload to the short video platform directly through their mobile phones, so the number of short videos on the short video platform is growing very fast. This makes the demand for an effective short video recommendation system very urgent. An effective short video recommendation system can improve user experience and user stickiness, thereby bringing huge commercial value to the platform. [0003] In recent years, many researchers have proposed video-based personalized recommendation methods. These methods can be classified into three categories: collaborative...
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IPC IPC(8): G06F16/9535G06F16/735G06N3/04G06Q10/04
CPCG06F16/9535G06F16/735G06N3/049G06Q10/04G06N3/047G06N3/045
Inventor 顾盼
Owner CHINA JILIANG UNIV