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Database establishment method and data recommendation method and device, equipment and storage medium

A database and data technology, applied in database design/maintenance, data processing applications, structured data retrieval, etc., can solve problems such as low accuracy, limited range of recommended information, and non-obvious regularity, and achieve accurate recommendation results

Active Publication Date: 2022-07-05
BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, in the process of realizing the research of the present invention, the inventors found that the above-mentioned technology has the following defects: if it relies on the user's personal historical data, the scope of the recommended information is very limited, and the prediction results of the recommended information often do not conform to the user's consumption behavior. development trend
Based on the statistics of massive user data, due to the large differences between users, the regularity is not obvious, so if the amount of statistical data is not enough, the accuracy of predicting user consumption behavior is low

Method used

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  • Database establishment method and data recommendation method and device, equipment and storage medium
  • Database establishment method and data recommendation method and device, equipment and storage medium
  • Database establishment method and data recommendation method and device, equipment and storage medium

Examples

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Embodiment 1

[0047] figure 1 It is a flow chart of a method for establishing a database provided in Embodiment 1 of the present invention. The method is applicable to the case of electronic commodity transactions, and the method can be executed by the database establishment means. The database establishment means can be implemented by software and / or hardware. like figure 1 As shown, the method includes:

[0048] S110. Obtain user historical data and commodity historical data from a data source.

[0049] Obtain offline data from a data source, and filter out the required user historical data and commodity historical data. The offline data includes at least: user behavior data, user attribute data, order data, and stock keeping unit data (SKU).

[0050] Further, the user history data may include user dynamic behavior data and user static attribute data; wherein, the user dynamic behavior data includes, for example, at least one of commodity browsing behavior, navigation positioning beha...

Embodiment 2

[0062] figure 2 It is a schematic flowchart of a database establishment method provided by the second embodiment of the present invention. This embodiment further describes the system on the basis of the above-mentioned first embodiment. like figure 2 As shown, the method includes:

[0063] S210. Obtain user historical data and commodity historical data from a data source.

[0064] S220. Extract user features according to a preset user dimension according to user history data and commodity history data to form a user feature vector of the user, where the user feature vector includes at least one commodity feature.

[0065] S230: Extract commodity features according to preset commodity dimensions according to user history data and commodity history data to form a primary commodity feature vector of the commodity, where the primary commodity feature vector includes at least one user feature.

[0066] S240. According to at least one core feature set in the preset commodity ...

Embodiment 3

[0081] image 3 It is a schematic flowchart of a data recommendation method provided by Embodiment 3 of the present invention. This embodiment is a data recommendation method introduced on the basis of the recommendation database of the above-mentioned embodiment. The method is suitable for the situation of data recommendation in commodity transactions, and the method can be executed by a data recommendation device. The data recommendation device may be implemented by software and / or hardware. like image 3 As shown, the data recommendation method includes:

[0082] S310. Obtain online recommendation requirements.

[0083] Online recommendation demand is the demand generated in real time based on the recommendation database to generate recommendation data. For example, when a user is selecting a product or generating an order, the logistics service provider needs to provide inventory forecast and actively recommend to users who open the client software. When commodity info...

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Abstract

The embodiments of the present invention disclose a database establishment method and a data recommendation method, an apparatus, a device and a storage medium. The database establishment method includes: acquiring user historical data and commodity historical data from a data source; extracting user characteristics according to the user historical data and commodity historical data according to a preset user dimension, so as to form a user's user characteristic vector, wherein the user characteristic vector is in the Including at least one commodity feature; according to user historical data and commodity historical data, commodity features are extracted according to preset commodity dimensions to form a primary commodity feature vector of the commodity, and the primary commodity feature vector includes at least one user feature; according to the preset commodity The association rule is to combine the primary commodity feature vectors to form secondary commodity feature vectors; store the user feature vectors, primary commodity feature vectors and secondary commodity feature vectors as recommendation data sets in the database. The present invention can improve the accuracy when predicting the user's attention information.

Description

technical field [0001] The embodiments of the present invention relate to a big data processing technology of a computer, and in particular, to a database establishment method and a data recommendation method and apparatus, equipment and storage medium. Background technique [0002] With the popularity of online sales, it is a development trend to effectively predict the behavior of users to purchase goods. [0003] In the prior art, the user's subsequent behavior data is generally predicted based on the user's own historical behavior data. For example, if a user often buys clothes in the past, it is predicted that the product information of clothes recommended to the user is in line with his consumption behavior. In addition to the technology of making statistics on the user's personal historical data to make predictions, the prior art generally uses the method of correlation rules to recommend information for the user. Generally, a large amount of user data is collected ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/02G06Q30/06G06F16/9535
CPCG06F16/21G06Q30/0202G06Q30/0631
Inventor 贺子昂张建峰李欣崔超张少南
Owner BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD
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