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Method and system for predicting travel intention of user, electronic equipment and storage medium

A prediction method and user technology, applied in the field of data processing, can solve problems such as the inability to rule out inconsistent orders between the booker and the traveler, lack of steps to identify the booking behavior, and low prediction accuracy, so as to optimize the travel experience and ensure reliability performance, and the effect of improving accuracy

Pending Publication Date: 2022-05-31
携程旅游信息技术(上海)有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved by the present invention is to overcome the defect in the prior art that there is no step of identifying the ordering behavior when predicting the user's travel intention, which leads to the inability to rule out orders inconsistent with the booker and the traveler, so that the prediction accuracy rate is low. Provide a Method, system, electronic device and storage medium for predicting user's travel intention

Method used

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  • Method and system for predicting travel intention of user, electronic equipment and storage medium
  • Method and system for predicting travel intention of user, electronic equipment and storage medium
  • Method and system for predicting travel intention of user, electronic equipment and storage medium

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Experimental program
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Embodiment 1

[0052] This embodiment provides a method for predicting a user's travel intention, refer to figure 1 , the prediction method includes the following steps:

[0053] S1. Obtain the historical travel data of the user within a preset historical time period;

[0054] S2. Based on the historical travel data, filter out the first travel data corresponding to the historical travel order that is consistent with the order booker and the order traveler;

[0055] S3. Based on the first travel data, construct a user travel intention prediction model;

[0056] S4. Obtain the pending travel data of the user;

[0057] S5. Input the travel data to be processed into the user travel intention prediction model, and output a prediction result used to represent the user travel intention.

[0058] Specifically, the historical travel data obtained in this embodiment may include historical travel order data (air ticket orders, hotel orders, ticket orders, etc.), and the user may purchase for himse...

Embodiment 2

[0061] refer to figure 2 , the method for predicting the user's travel intention in this embodiment is a further improvement to Embodiment 1.

[0062] In an implementable manner, step S3 includes:

[0063] S301. Classify the first trip data, and obtain one trip data belonging to the same single trip;

[0064] S302 , constructing a user travel intention prediction model based on the one-time trip data.

[0065]Specifically, a user may generate several air ticket orders and accommodation orders in one trip, or may only generate a single air ticket order or accommodation order. Through categorization processing, orders belonging to the same single trip can be packaged to obtain complete one trip data.

[0066] Combining a complete itinerary to predict the user's travel intention, it solves the problem that it is difficult to judge the user's travel intention based on a single order, and improves the accuracy of the prediction model.

[0067] In an implementable manner, step ...

specific Embodiment approach

[0103] Figure 4 This is a flowchart of a method for predicting a user's travel intention provided in this embodiment. The data is obtained in real time from the message queues of each business unit for packaging; the packaged data is then feature processed; the real-time model obtains the processed data for training; the demand-side system predicts the user's travel intention by calling the real-time model. The specific implementation is as follows:

[0104] (1) Get data

[0105] Synchronize order data, page operation behavior data, and user location data from various business units. The data entering the offline data pool will be packaged into the offline itinerary; the data entering the real-time data pool will enter the real-time itinerary splicing (the code is roughly the same as the offline), and the user's itinerary data will be obtained.

[0106] (2) Feature processing

[0107] As described above, the feature data may include, but not limited to, travel feature dat...

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Abstract

The invention discloses a user travel intention prediction method and system, electronic equipment and a storage medium. The prediction method comprises the following steps: acquiring historical travel data of a user in a preset historical time period; based on the historical travel data, screening out first travel data corresponding to a historical travel order in which an order booking person is consistent with an order traveler; constructing a user travel intention prediction model based on the first travel data; obtaining to-be-processed travel data of a user; and inputting the to-be-processed travel data into the user travel intention prediction model, and outputting a prediction result for representing the user travel intention. According to the method and the device, orders of which order bookers are inconsistent with actual travelers are eliminated by identifying the ordering behavior, so that the reliability of the sample data corresponding to each traveler is ensured, and the accuracy of the prediction model is improved. The prediction result of the user travel intention can be used for formulating a recommendation strategy, the service quality of the platform is improved, and the travel experience of the user is optimized.

Description

technical field [0001] The present invention relates to the technical field of data processing, and in particular, to a method, system, electronic device and storage medium for predicting a user's travel intention. Background technique [0002] At present, when judging the user's travel intention, each platform directly obtains all the order data of the same user, and performs a series of processing on these order data (such as removing abnormal data, segmenting words, etc.) to obtain sample data that meets the training conditions; however, The travel intention prediction results based on these sample data generally have the problem of low prediction accuracy and inability to accurately determine the travel intention of a user for a trip. SUMMARY OF THE INVENTION [0003] The technical problem to be solved by the present invention is to overcome the defect that the prior art lacks the step of identifying the booking behavior when predicting the user's travel intention, res...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06K9/62G06F16/9535
CPCG06Q10/04G06F16/9535G06F18/241
Inventor 吕晴吴克贤陈海强陆刚邹宇
Owner 携程旅游信息技术(上海)有限公司
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