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Store and commodity recommendation method, device, equipment and readable storage medium

A product recommendation and store technology, applied in the field of big data, can solve problems such as personal information analysis and inaccurate prediction of user shopping consumption demand

Inactive Publication Date: 2019-11-05
重庆仙桃前沿消费行为大数据有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Most of the existing precision marketing methods recommend products and stores based on historical shopping data and search history, without comprehensive analysis of the user's personal information, making the prediction of the user's shopping consumption demand inaccurate.

Method used

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  • Store and commodity recommendation method, device, equipment and readable storage medium
  • Store and commodity recommendation method, device, equipment and readable storage medium
  • Store and commodity recommendation method, device, equipment and readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046] Such as figure 1 As shown, this embodiment provides a store and product recommendation method, including the following steps:

[0047] Step S1, obtaining data to be predicted, the data to be predicted includes user data and store commodity data;

[0048] Among them, user data includes data such as age, occupation, gender, location information, marital status, child status, historical shopping information, and search history; store product data includes store type, store name, store location, product name and other data.

[0049] Step S2, obtaining a user feature vector representing user data and a store product feature vector representing store product data according to the user data and store product data in step S1;

[0050] Step S3, input the user feature vector and store product feature vector into the neural network model, and output the probability vector;

[0051] Step S4, recommending corresponding stores and commodities according to the probability vector.

...

Embodiment 2

[0081] Corresponding to the above method embodiment, this embodiment also provides a store and product recommending device. The store and product recommending device described below and the store and product recommending method described above can be referred to in correspondence.

[0082] see Image 6 As shown, the device includes the following modules: data acquisition module 101, data processing module 102, model module 103 and model training module 104;

[0083] Among them, the data acquisition module 101 is used to acquire user data and store product data; this embodiment can collect user data through mobile phones or mobile terminals; the collected user data includes WIFI MAC addresses such as mobile phones or mobile terminals, and according to the MAC The address and IMEI are compared with the big data in the cloud to locate the user's identity and obtain the corresponding mobile phone number, daily APP and other information.

[0084] The data processing module 102 is ...

Embodiment 3

[0091] Corresponding to the above method embodiment, this embodiment also provides a store and product recommendation device. The store and product recommendation device described below and the store and product recommendation method described above can be referred to in correspondence.

[0092] see Figure 7 As shown, the store and product recommended equipment include:

[0093] memory D l for storing computer programs;

[0094] The processor D2 is configured to implement the steps of the method for recommending stores and commodities in the above method embodiments when executing the computer program.

[0095] Specifically, please refer to Figure 8 , is a schematic diagram of the specific structure of the store and product recommendation equipment provided in this embodiment. The store and product recommendation equipment may have relatively large differences due to different configurations or performances, and may include one or more than one processor (central processin...

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Abstract

The invention discloses a store and commodity recommendation method, a store and commodity recommendation device, equipment and a readable storage medium. The store and commodity recommendation methodcomprises the following steps that S1, acquiring to-be-predicted data, wherein the to-be-predicted data comprises user data and store commodity data; S2, obtaining a user feature vector representingthe user data and a store commodity feature vector representing the store commodity data according to the user data and the store commodity data in the step S1; S3, inputting the user feature vector and the store commodity feature vector into a neural network model, and outputting a probability vector; and S4, predicting the shopping demand of the user according to the probability vector, and recommending corresponding shops and commodities of the user within a certain distance range.

Description

technical field [0001] The present invention relates to the field of big data, in particular to a store and commodity recommendation method, device, equipment and readable storage medium. Background technique [0002] With the continuous development of society, cities will carry more population in the future, so the sustainable development of cities is particularly important. The starting point of smart city is networking and digitization under the development of modern information technology. The ultimate goal is to raise it to the level of integration, clustering, and collaborative management, and combine it with green and sustainable development to build a livable urban environment. Based on the new generation of information technology such as the Internet of Things, cloud computing, big data, and spatial and geographical information integration, smart cities can improve urban services, public safety, environmental protection, and people's livelihood by sensing, analyzing...

Claims

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Application Information

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IPC IPC(8): G06Q30/06G06Q30/02G06K9/62
CPCG06Q30/0631G06Q30/0256G06Q30/0269G06Q30/0201G06F18/23213G06F18/214
Inventor 周羽徐斌
Owner 重庆仙桃前沿消费行为大数据有限公司
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