Reference place recommendation method, apparatus and device, and storage medium

Abstract

The invention provides a reference place recommendation method and device, equipment and a storage medium. The method comprises the steps: obtaining a reference place obtaining request initiated by atarget user terminal, the reference place obtaining request carrying the current position of the target user terminal; determining a plurality of target reference places within a preset range from thecurrent position; inputting the identifier of the target area to which the current position belongs and the identifiers of the plurality of target reference places into a pre-trained vector generation model to obtain vector representation of the target area to which the current position belongs and vector representation of the plurality of target reference places, wherein the vector generation model is obtained by training a preset model by taking an association relationship between regions of various sizes and each reference place as a training sample; determining a to-be-recommended reference place recommended to the target user terminal according to the similarity between the vector representation of the target area to which the current position belongs and the vector representation ofeach of the plurality of target reference places.

Description

technical field [0001] The present application relates to the technical field of information processing, in particular to a reference location recommendation method, device, equipment and storage medium. Background technique [0002] In the existing online car-hailing scenario, it is necessary to recommend a pick-up location for the user based on the car order issued by the user. Among them, recommending the pick-up location to the user is generally realized through a ranking model. Among them, the establishment of the ranking model mainly relies on the statistical characteristics of historical car orders, such as order popularity and boarding popularity. However, for different geographic regions, the distribution of car orders is uneven, and the statistical characteristics cannot fully reflect the correlation between users and boarding locations in the billing area, which leads to the fact that the recommended boarding locations for users The accuracy is lower. Contents...

AI Technical Summary

Problems solved by technology

However, for different geographic regions, the distribution of car orders is uneven, and the statistical characteristics cannot fully reflect the correlation between users and boarding locations in the billing area, which leads to the fact that the recommended boarding locations for users less accurate

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