Vehicle using traveling behavior prediction method and device, server and storage medium
A prediction method and a technology of a prediction device, which are applied in the field of data processing, can solve problems such as the inability to realize accurate prediction of online car-hailing services, and achieve the effect of improving user experience and improving accuracy
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Embodiment 1
[0026] Figure 1a It is a flow chart of the method for predicting travel behavior by car provided by Embodiment 1 of the present invention. This embodiment is applicable to the situation where the user to be predicted performs personalized user travel prediction, and the method can be executed by a device for predicting travel behavior by car , wherein the device may be implemented by means of software and / or hardware, and integrated into the server. Wherein, the server is a background server that provides car travel services. refer to Figure 1a , the prediction method provided in this embodiment specifically includes:
[0027] S110. Perform scene feature monitoring on the user to be predicted, so as to obtain scene features of the instant application scene where the user to be predicted is located.
[0028] In this embodiment, the instant application scene is the application scene where the user to be predicted is currently located, such as a weather scene, a traffic condit...
Embodiment 2
[0065] figure 2 It is a flow chart of the method for predicting travel behavior by car provided in Embodiment 2 of the present invention. This embodiment is embodied on the basis of the above-mentioned embodiments, refer to figure 2 , the prediction method provided in this embodiment specifically includes:
[0066] S210. Obtain user profile features of the target user.
[0067] Wherein, the specific manner of acquiring the user portrait features of the target user is the same as the manner of acquiring the user portrait features described in Embodiment 1, and will not be described here.
[0068] S220. Acquire at least two target sub-portrait features from the user portrait features.
[0069] Optionally, the features of the target sub-portrait are more important features of the user picture features, such as gender, industry, age, family location, and company location. Important features can be set according to actual conditions.
[0070] S230. Determine the user corresp...
Embodiment 3
[0093] image 3 The flow chart of the method for predicting car travel behavior provided by Embodiment 3 of the present invention. This embodiment is embodied on the basis of the above-mentioned embodiments. Refer to image 3 , the prediction method provided in this embodiment specifically includes:
[0094] S310. Select a candidate application scenario.
[0095] Among them, the candidate application scenarios are all application scenarios that may have car demand.
[0096] S320. Determine the information gain of each candidate application scenario for the historical car travel decision.
[0097] Specifically, different candidate application scenarios have different impacts on historical car travel decisions. For example, the result of car travel in a candidate application scenario has two values. All car travel decisions use car travel services, and the other value corresponds to the car travel decisions of users who have placed orders for cars in history, all of which are...
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