Tourist preference learning system and method based on sightseeing behavior

Abstract

The invention proposes a tourist preference learning system based on a sightseeing behavior. The system comprises an iBeacon module, an intelligent module, an application and a server; the iBeacon module is used for identifying position information of sightseeing spots in a scenic region or exhibits inside an exhibition hall; the application is used for receiving and parsing a broadcast data packet of the iBeacon module, calculating the distance between the intelligent terminal and iBeacon equipment, and identifying a current sightseeing spot of a tourist; the intelligent terminal is used foracquiring sightseeing behavior data of a certain sightseeing spot of the tourist and uploading the sightseeing behavior data to the server; and the server is used for processing and storing the sightseeing behavior data of the tourist, and acquiring the preference of the tourist to different sightseeing spots according to a preference learning model. According to the method of the invention, the iBeacon module and the intelligent terminal are combined to obtain the fine-grained tourist sightseeing behavior of the tourist, and the fine-grained sightseeing preference of the tourist for all sightseeing spots in a certain scenic region can be obtained.

Description

technical field [0001] The invention relates to the technical field of mobile Internet terminals, in particular to a tourist preference learning system and method based on tour behavior. Background technique [0002] At present, the number of check-in times or the order of check-in at the points of interest is often used in the relevant research on tourists’ travel preference learning as the preference degree of tourists for points of interest. The higher the degree of preference. Most of the relevant research on tourist preference learning uses location-based social network datasets, which can only learn tourists’ coarse-grained preferences for scenic spots; secondly, in the actual environment, tourists do not share their location information in real time, so The check-in data set only contains part of the location information of tourists, resulting in sparse data and insufficient evaluation of travel preferences; and tourists often rate and evaluate scenic spots after the...

AI Technical Summary

Problems solved by technology

[0003] In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a tourist preference learning system and method based on tour behavior to solve the problem of lack of real-time and objectivity in obtaining preferences in the current technology, and the lack of comprehensive and detailed preference learning. And other issues

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