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Causal inference-based online shopping behavior analysis method and system

An online shopping and behavior analysis technology, applied in the field of online shopping behavior analysis based on causal inference, can solve the problems of no effect noise, low value density, and large amount of social behavior data information, so as to reduce interference and improve accuracy Effect

Active Publication Date: 2019-09-17
GUANGDONG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

However, the volume of social behavior data information is huge, the data types are various, the value density is low, and there are a large number of useless noise features, and the behavior feature data cannot be directly used
In addition, social behavior data and shopping behavior data belong to different fields, and the fusion of cross-domain features is also one of the real challenges.

Method used

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  • Causal inference-based online shopping behavior analysis method and system
  • Causal inference-based online shopping behavior analysis method and system
  • Causal inference-based online shopping behavior analysis method and system

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Embodiment Construction

[0029] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples.

[0030] Such as figure 1 As shown, the present invention discloses a method and system for analyzing online shopping behavior based on causal inference, which specifically includes the following steps:

[0031] Step 1, input the social behavior data of all users, there are n users in total, respectively v 1 , v 2 ,...,v n ∈V. The social data collection module divides the social behavior data into n social behavior subsets according to the user ID, respectively S 1 , S 2 ..., S n ∈S, each user has a corresponding time series social behavior set. Each behavior set contains several text data, corresponding to the user's text information at a certain moment, and uses text analysis technology to generate feature vectors for all texts. Use the time series neural network LS...

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Abstract

The invention relates to the fields of data mining, social networks and causal inference, and discloses a causal inference-based online shopping behavior analysis method and system, which can fully obtain the user behavior characteristics and the interest preferences by fusing the user attribute characteristics, the social behavior characteristics, the historical shopping behavior characteristics, the user relationships and other multi-level and cross-domain characteristics. Through a reasonably designed analysis system, the useless characteristics are removed by using a causal network model, the interference of the noise characteristics is reduced, the causal properties and the behavior motivation of the user behaviors can be explained, and the accuracy of the user shopping behavior prediction is improved.

Description

technical field [0001] The invention relates to the fields of data mining, social network and causal inference, in particular to a method and system for analyzing online shopping behavior based on causal inference. Background technique [0002] The continuous development of Internet technology makes online shopping more and more popular. In order to provide users with real-time recommended products and achieve precise marketing, it is necessary to have a deep understanding of users' shopping motivations and shopping patterns, as well as users' actual needs and hobbies. The current recommendation system usually only infers the user's interest preferences and needs based on the user's product browsing records, historical shopping information, product reviews and other shopping-related data. timely. [0003] The user's social behavior provides a lot of useful data for the analysis of user interest preferences and behavior patterns. Social networks not only move the unobserva...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06N3/04G06N3/08G06Q50/00
CPCG06Q30/0201G06Q30/0203G06Q50/01G06N3/08G06N3/044G06N3/045Y02D10/00
Inventor 郝志峰黎伊婷蔡瑞初温雯王丽娟陈炳丰
Owner GUANGDONG UNIV OF TECH
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