High potential user purchase intention prediction method based on big data user behavior analysis

A big data and user technology, applied in feature construction, model design and optimization, sample definition in data analysis, data division field, can solve problems such as ignoring user behavior characteristics, and achieve the effect of improving prediction accuracy

Active Publication Date: 2022-03-22
上海普瑾特信息技术服务股份有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This scheme simply uses the user's rating information and ignores the user's own behavioral characteristics.

Method used

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  • High potential user purchase intention prediction method based on big data user behavior analysis
  • High potential user purchase intention prediction method based on big data user behavior analysis
  • High potential user purchase intention prediction method based on big data user behavior analysis

Examples

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Embodiment 1

[0040] In real online shopping, we often choose the items we like by browsing, following or adding to the shopping cart, but when purchasing a certain item, we often browse multiple products. Therefore, the purpose of this example is to predict whether there is a purchase intention and which item has a higher purchase intention through the user's historical behavior. The following symbols are defined in the example:

[0041] S: The complete set of products provided;

[0042] P: Candidate commodity subset, P is a subset of S;

[0043] U: user collection;

[0044] A: User behavior data collection for S;

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Abstract

The present invention claims to protect a high-potential user purchase intention prediction method based on big data user behavior analysis, including: 101 data preprocessing, performing preprocessing operations on the e-commerce user historical behavior data set; 102 sample definition and marking, according to user Historical consumption behavior, constructing samples with interactive user-product pairs as keywords; 103 training set test set division, using time window division method to divide historical data into training set and test set; 104 feature construction, historical behavior of users Data feature engineering construction; 105 algorithm design and implementation, first select the feature group feature and process the unbalanced data of the data set, and then the present invention proposes a two-layer model iterative learning algorithm to predict the final result. The present invention establishes a prediction model based on the historical behavior data of e-commerce users with a time span of 45 days, so as to predict whether the user will place an order for a product in the candidate product set P in the next 5 days.

Description

technical field [0001] The invention belongs to the fields of machine learning and data analysis, and in particular relates to technologies such as sample definition, data division, feature construction, model design and optimization in data analysis. Background technique [0002] Online shopping e-commerce, while maintaining rapid development, has accumulated hundreds of millions of loyal users and accumulated massive real data. How to find out the rules from historical data, predict the future purchase needs of users, and let the most suitable products meet the people who need them most are the key issues in the application of big data in precision marketing, and it is also the intelligent upgrade of all e-commerce platforms the core technologies required. [0003] Recommendation algorithms can be roughly divided into three categories: content-based recommendation algorithms, collaborative filtering recommendation algorithms, and knowledge-based recommendation algorithms....

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q30/02G06K9/62
CPCG06Q30/0201G06Q30/0202G06F18/211G06F18/23
Inventor 王进杨阳周瑞港孙开伟欧阳卫华邓欣陈乔松
Owner 上海普瑾特信息技术服务股份有限公司
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