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

A technology of behavior analysis and prediction methods, applied in data analysis, sample definition, feature construction, model design and optimization, data division, and can solve the problem of ignoring user behavior characteristics.

Active Publication Date: 2018-04-20
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 buying intention prediction method based on big data user behavior analysis
  • High potential user buying intention prediction method based on big data user behavior analysis
  • High potential user buying 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 invention provides a high potential user buying intention prediction method based on big data user behavior analysis. The high potential user buying intention prediction method comprises the following steps: 101 data preprocessing: the historical behavior data set of the e-commerce user is preprocessed; 102 sample defining and marking: samples are constructed with the interacted user product pairs to act as the keywords according to the historical consumption behavior of the user; 103 division of a training set and a test set: the historical data are divided into the training set and the test set by using a time window division method; 104 feature construction: feature engineering construction of the historical behavior data of the user is performed; and 105 algorithm design and implementation: feature selection of the feature group and unbalanced data processing of the data set are performed and then the final result of two-layer model iterative learning algorithm prediction is put forward. The prediction model is established on the basis of the historical behavior data of the e-commerce user of the time span of 45 days so that whether the user places an order of the commodityin the candidate commodity set P in the following 5 days can be predicted.

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 needed. [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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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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