A user activity prediction model training method, system and device and a storage medium
A technology of user activity and prediction model, which is applied in the field of user activity prediction model training methods, equipment and storage media, and systems. It can solve problems such as supervised learning, easy loss of a large number of sample information, and huge differences in the proportion of silent users. Improve overall activity and reduce costs
Active Publication Date: 2019-04-16
江苏满运物流信息有限公司
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Problems solved by technology
However, in the existing activity prediction methods, if the activity prediction model is used, due to the huge difference in the proportion of silent users and non-silent users, and the lack of sample balance methods, it is easy to lose a large amount of sample information, and it is impossible to directly perform a simple and effective method. Supervised learning, and in conventional prediction methods, there is no mature application of feature intersection and feature depth, and the accuracy of the activity prediction model cannot be guaranteed
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[0039] Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0040] Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities ...
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The invention provides a user activity prediction model training method, system and device and a storage medium, and the method comprises the steps: collecting historical data of a user on a platform,and dividing the user into a first user with a determined activity label and a second user with an undetermined activity label; Adding the historical data and the activeness label of the first user into a first training set, and training a pre-classification model by adopting the first training set; Inputting the historical data of the second user into a pre-classification model, and adding an activeness label for the second user according to an output result of the pre-classification model; And adding the historical data and the activity degree label of the first user and the historical dataand the activity degree label of the second user into a second training set, and training the activity degree prediction model by adopting the second training set. By adopting the scheme of the invention, a semi-supervised learning method is adopted, so that less sample data in the unbalanced samples can be effectively and repeatedly utilized, and an activity prediction model with high accuracy can be quickly and effectively constructed.
Description
technical field [0001] The present invention relates to the technical field of machine learning, in particular to a user activity prediction model training method, system, device and storage medium. Background technique [0002] The life process of platform users usually goes through the registration stage, active stage, loss stage and silent stage. The number of users in the silent stage is large and cannot bring value to the platform. It is necessary to follow up with push marketing, SMS marketing, customer service return visits and other methods to retain some users who can be activated with a high probability, so as to improve the overall activity of platform users. However, in the existing activity prediction methods, if the activity prediction model is used, due to the huge difference in the proportion of silent users and non-silent users, and the lack of sample balance methods, it is easy to lose a large amount of sample information, and it is impossible to directly p...
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Login to View More IPC IPC(8): G06Q10/04G06Q10/06G06Q10/08G06Q30/02
CPCG06Q10/04G06Q10/0639G06Q10/083G06Q30/0201
Inventor 王东沙韬伟罗竞佳邓金秋刘祥
Owner 江苏满运物流信息有限公司