Store arrival prediction method and device, readable storage medium and electronic equipment

Abstract

The embodiment of the invention provides a store arrival prediction method and device, a readable storage medium and electronic equipment. The method comprises the steps of obtaining a search requestinitiated by a user terminal; performing feature extraction on the search request, and determining features of a user of the user terminal, features of each store in a store list corresponding to thesearch request and user-store cross features; inputting the characteristics of the user, the characteristics of each shop and the user-shop cross characteristics into a pre-trained shop arrival probability prediction model, and determining the probability that the user arrives at each shop; and determining whether the user arrives at a target store or not according to the probability that the userarrives at each store. According to the invention, the target store to which the user arrives is determined according to the probability of each store output by the store arrival probability prediction model, so that accurate positioning of the current position of the user is realized, and the reliability of the stores near the current position recommended to the user by the user terminal is improved.

Description

technical field [0001] The present application relates to the technical field of information processing, and in particular to a store arrival prediction method, device, readable storage medium and electronic equipment. Background technique [0002] With the development of the mobile Internet, people can easily access the Internet through mobile devices to obtain services, and a number of O2O (Online-to-Offline) local life-oriented services (such as: O2O nearby search) have emerged, which have a great impact It facilitates people's life. Taking O2O nearby search as an example, users can view food, entertainment and other life information near the previous location through this search function. According to the distance between the location and the merchants marked with latitude and longitude, the merchants whose distance meets the user's screening range are sorted according to a certain algorithm to obtain the screening results, and the screening results are returned to the ...

AI Technical Summary

Problems solved by technology

[0003] However, there is a problem in the specific implementation process of the above-mentioned search function: the screening results are overly dependent on the user's location. Merchants cannot be used as screening results and returned to users, which seriously affects users' decision-making and experience

In actual situations, the user's real-time positioning is greatly affected by the network conditions. When the user initiates an O2O nearby search in some large buildings (for example: in a shopping mall), the floor information of the large building cannot be located due to ordinary positioning technology. , and the mobile device cannot receive GPS signals indoors, and can only rely on the base station of the network operator to realize the real-time positioning of the user, but the real-time positioning of the user obtained by relying on the base station of the network operator has drift phenomenon, and the real-time positioning of the user indoors cannot be guaranteed. Positioning meets high precision requirements

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