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Data recommendation method

A technology for data recommendation and type parameters, applied in data processing applications, commerce, instruments, etc., can solve problems such as inaccurate prediction results of big data, and achieve the effect of reducing promotion costs and improving accuracy

Active Publication Date: 2020-05-08
杭州聚效科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The problem to be solved by the present invention is to provide a data recommendation method aiming at the technical problem of inaccurate big data prediction results due to submerged potential needs of users

Method used

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Embodiment 1

[0031] An embodiment of the present invention provides a data recommendation method, figure 1 is a flow chart of a method for using data recommendation provided according to an embodiment of the present invention, such as figure 1 As shown, a data recommendation method includes:

[0032] S101, acquiring the identity of the user;

[0033] S102. Obtain a user superior record;

[0034] S103, input the user superior record into the pre-trained potential demand generation model, and output the behavior subject's potential demand prediction result;

[0035] Specifically, combined with the SaaS model, the store logs in to the system after subscribing, obtains the buyer ID through the seller_nick code, obtains the authorization binding API, synchronizes the customer back-end data with the RDS data, and obtains the buyer's last purchase from the RDS version of the Alibaba Cloud database. Merge the order data, clean the data, import it into the model, and find the matching behavior s...

Embodiment 2

[0067] The embodiment of the present invention provides an application scenario of the data recommendation method. The above new baby is the actual scene, and the new baby means that there is no historical information, so it is necessary to use a relative analysis scheme to operate on the relevant content. By looking for similar babies or the historical records of similar babies, the identified potential needs are prioritized, the best is selected, and the continuous data accumulation generates a more comprehensive model.

[0068] A data recommendation method, comprising: obtaining the identity of the user; obtaining the user's superior record; inputting the user's superior record into a pre-trained potential demand generation model, and outputting a behavior subject's potential demand prediction result; wherein, the user's superior record The step of inputting the pre-trained potential demand generation model includes generating the model according to the potential demand, inc...

Embodiment 3

[0084] The embodiment of the present invention provides a data recommendation method. Taking double 11 ladder marketing as an example, the system combines the ranking needs of the double 11 preheating venue, and can distribute the number of marketing people according to the percentage of the day, so that the number of preheating, purchasing, and collections will increase. Or decreasing or other waveform trends, better to achieve the double 11 warm-up purpose.

[0085] According to the big data statistics of previous years, November 11th is the outbreak day, November 10th is the consolidation day, the warm-up period is from November 01st to November 10th, and the small climax is the first day after the start of November 01st. The most recent weekend before the warm-up period on November 1st is the pre-day.

[0086]During the entire hot marketing period, data recommendation needs to analyze the user's historical records according to the store's settings, and recommend suitable i...

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Abstract

The invention provides a data recommendation method which is characterized by comprising the following steps: acquiring an identity label of a user; obtaining a user superior record; inputting the user superior record into a pre-trained potential demand generation model, and outputting a potential demand prediction result of a behavior subject, wherein the step of generating the model according tothe potential demand comprises at least one of the following steps: receiving a fixed-area analysis instruction, dividing a label into fixed-area areas, taking a limited label value range as a judgment basis for the fixed-area areas, determining a behavior subject when a user superior record belongs to one of the areas in label division, and matching subordinate records of the same behavior subject in a sample library; and receiving a relative analysis instruction, dividing the labels into relative regions, taking the interval label value range as a judgment standard by the relative regions,determining the behavior subjects when the user superior records meet the relative regions, and matching subordinate records of the same behavior subjects in a sample library.

Description

technical field [0001] The present invention relates to the field of AI data analysis and deduction, specifically a data recommendation method. Background technique [0002] With the development of science and technology, more and more analysis and forecasting based on big data appear; however, the analysis and statistics of big data are tedious and cumbersome, difficult to operate manually, and high in operating costs, which is not conducive to economic cost saving, and the inter-industry Disconnected, statistical forecasting methods are messy, and big data cannot be effectively used. Due to insufficient funds, some private enterprises or small industries still rely on the subjective judgment of people, resulting in the defects of poor accuracy, slow update awareness, uneven personnel levels, non-reproducibility, and small effective range. [0003] At present, with the continuous accumulation of big data, more and more data has formed a statistical problem, and the analysi...

Claims

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Application Information

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IPC IPC(8): G06Q30/02G06Q30/06
CPCG06Q30/0202G06Q30/0631
Inventor 王旭春
Owner 杭州聚效科技有限公司
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