Vehicle recommendation method and device, equipment and storage medium

A recommendation method and vehicle technology, applied in the computer field, can solve the problems of reduced user experience, low coverage, poor matching of recommended results, etc., and achieve the effect of improving user experience and improving matching.

Active Publication Date: 2019-11-15
上海乐享似锦科技股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In the prior art, when car sharing platforms recommend vehicles to users, they are usually calculated directly based on statistical data such as popularity and user clicks, and the granularity is relatively coarse. At the same time, the recommendations are m

Method used

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  • Vehicle recommendation method and device, equipment and storage medium
  • Vehicle recommendation method and device, equipment and storage medium
  • Vehicle recommendation method and device, equipment and storage medium

Examples

Experimental program
Comparison scheme
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Example Embodiment

[0031] Example one

[0032] figure 1 It is a flowchart of a vehicle recommendation method in the first embodiment of the present invention. The embodiment of the present invention is applicable to the case of recommending vehicles to users in a shared car rental platform. The method is executed by a vehicle recommendation device, which uses software and / Or hardware implementation, and specifically configured in an electronic device with certain data computing capabilities, where the electronic device can be a server or a personal computer.

[0033] Such as figure 1 A vehicle recommendation method shown includes:

[0034] S110: Obtain a user feature vector of the current user, and determine the vehicle feature vector of the vehicle to be recommended.

[0035] Among them, the user feature feature vector is used to characterize feature information corresponding to different users, so as to distinguish different users. Exemplarily, the characteristic information corresponding to the use...

Example Embodiment

[0059] Example two

[0060] figure 2 It is a flowchart of a vehicle recommendation method in the second embodiment of the present invention. The embodiment of the present invention is optimized and improved on the basis of the technical solutions of the foregoing embodiments.

[0061] Further, the operation "determine the long-term behavior vector of the current user in the first time period based on the historical interaction information of the current user on the historically recommended vehicle" is refined into "recommend the vehicle based on the history in the first The corresponding historical interaction information in the time period respectively determines the comprehensive interaction times of the interactive behavior of each of the historical recommended vehicles by the current user; and filters the set number of historical recommendations according to the comprehensive interaction times corresponding to each historical recommended vehicle Vehicles; determine the long-te...

Example Embodiment

[0096] Example three

[0097] image 3 It is a flowchart of a vehicle recommendation method in the third embodiment of the present invention. The embodiment of the present invention is optimized and improved on the basis of the technical solutions of the foregoing embodiments.

[0098] Further, the operation "determine the short-term behavior vector of the current user in the second time period according to the historical interaction information of the current user on the historically recommended vehicle" is refined into "recommend the vehicle based on the history in the second The historical interaction information corresponding to the time period counts the number of interactions of the current user with the historical recommended vehicle at different times; according to the total number of interactive behaviors of the current user with the historical recommended vehicle within a preset time period The number of interactions is used to filter historical recommended vehicles; the ...

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Abstract

The embodiment of the invention discloses a vehicle recommendation method and device, equipment and a storage medium. The method comprises the steps of obtaining a user feature vector of a current user and determining a vehicle feature vector of a to-be-recommended vehicle; determining a long-term behavior vector of the current user in the first time period and a short-term behavior vector of thecurrent user in the second time period according to the historical interaction information of the current user for the historical recommended vehicle; inputting the user feature vector, the vehicle feature vector, the long-term behavior vector and the short-term behavior vector into a DeepFM model to obtain a behavior prediction score of the interaction behavior of the current user on the to-be-recommended vehicle; and recommending the vehicle to the current user according to the behavior prediction score. According to the technical scheme provided by the embodiment of the invention, the long-term and short-term behavior preferences of the user can be tracked in the use process of the DeepFM model, so that the personalized requirements of the user are met during vehicle recommendation, thematching degree between the vehicle recommendation result and the user is improved, and the user experience is improved.

Description

technical field [0001] The embodiments of the present invention relate to the field of computer technology, and in particular to a vehicle recommendation method, device, equipment and storage medium. Background technique [0002] As a new economic form, the sharing economy temporarily transfers the idle resources of the supply side through the sharing platform, improves the utilization rate of assets, and creates value for the demand side. [0003] In the prior art, when car sharing platforms recommend vehicles to users, they are usually calculated directly based on statistical data such as popularity and user clicks, and the granularity is relatively coarse. At the same time, the recommendations are mainly based on public interest and general preference vehicles, and the recommendation results are easily affected by group popularity. The coverage rate is low, which makes the recommendation result poorly matched with the user and reduces the user experience. Contents of th...

Claims

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Application Information

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IPC IPC(8): G06F16/9535G06K9/62G06Q30/06
CPCG06Q30/0645G06F16/9535G06F18/214
Inventor 李斓朱思涵罗欣
Owner 上海乐享似锦科技股份有限公司
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