Recommendation system and method for non-high-frequency consumption industry

A recommendation system and recommendation method technology, applied in the field of recommendation systems in non-high-frequency consumer industries, can solve the problems of less user information and the inability to achieve accurate recommendations, and achieve the effects of reducing interference information, reducing subjective influence, and improving accuracy

Pending Publication Date: 2021-03-09
重庆众帮车信息科技有限公司
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] One of the purposes of the present invention is to provide a recommendation system for non-high-frequency consumption industries to solve the technical problem in the prior art that less user information is collected in non-high-frequency consumption industries and accurate recommendations cannot be realized

Method used

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  • Recommendation system and method for non-high-frequency consumption industry

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Embodiment

[0043] Recommender systems for non-high-frequency consumer industries, as attached figure 1 As shown, it includes a server, a user terminal and a management terminal. The server includes a database, a data call module, a feature generation module, a preference calculation module, a behavior judgment module, a preference correction module, a similarity screening module and a recommendation matching module.

[0044] The user terminal is for the user to use, such as a mobile phone, and the corresponding service software is loaded on the user terminal. In this embodiment, the second-hand car industry is used as an example of a non-high-frequency consumption industry for illustration, and the corresponding second-hand car industry is installed on the corresponding user terminal. Purchase service software. The user terminal is used to obtain user information generated by user registration and upload user information. User information includes user account, gender, age, occupation an...

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Abstract

The invention relates to the technical field of data analysis and processing, in particular to a recommendation system and method for the non-high-frequency consumption industry. The system comprisesa server which is used for carrying out the judgment according to an operation behavior and a preset ordering behavior, obtaining service follow-up information when the ordering behavior exists in theoperation behavior, correcting the preference relationship weight according to the service follow-up information and the product feature, otherwise, obtaining a to-be-matched feature of the current user, screening a similar user group according to the to-be-matched feature and preset user information, and correcting the preference relationship weight corresponding to the current user according tothe preference relationship weight in the similar user group; and the server is also used for calling the product information of the to-be-sold product according to the product characteristics when obtaining the purchase appeal, and generating a product recommendation list according to the preference relationship weight and the product information. By the adoption of the scheme, the technical problem that in the prior art, recommendation cannot be accurately achieved due to the fact that data samples collected in the non-high-frequency consumption industry are low can be solved.

Description

technical field [0001] The invention relates to the technical field of data analysis and processing, in particular to a recommendation system and method for non-high-frequency consumer industries. Background technique [0002] In modern society, in order to promote the success rate of transactions, product recommendations are often made according to users’ purchase demands. Since product recommendations are based on user attribute analysis, and the accuracy of attribute analysis depends on the number of data samples, due to high-frequency consumer industries, corresponding users have more operational behaviors and collect more data samples of user behaviors, so recommendation systems and recommendation methods are mostly applied to network platforms in high-frequency consumption industries, that is, network platforms where users have high-frequency consumption behaviors, such as Taobao , Douyin, etc. [0003] For non-high-frequency consumption industries, such as second-han...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/06G06K9/62G06F16/9535G06F16/958
CPCG06Q30/0631G06F16/9535G06F16/958G06F18/22
Inventor 周彬盛荣
Owner 重庆众帮车信息科技有限公司
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