User big data processing method and system for personalized recommendation

A big data processing and user technology, applied in the direction of electrical digital data processing, special data processing applications, digital data information retrieval, etc.

Inactive Publication Date: 2022-08-05
黄建
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, the development of personalized recommendation relies on user interest analysis and mining. However, with the increasing number of users, how to accurately and comprehensively analyze user interest to reduce the error of interest analysis and mining is a difficult problem that has not yet been overcome.

Method used

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  • User big data processing method and system for personalized recommendation
  • User big data processing method and system for personalized recommendation
  • User big data processing method and system for personalized recommendation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment approach F1

[0040] Embodiment F1: If the user experience feedback identification thread is a lightweight thread, it is determined that each feedback event in the product push activity feedback content corresponding to the pushed digital service product does not have feature identification. Through multiple groups of feedback contents of the product push activities for the pushed digital service products, the distribution of each item in the pushed digital service products in the pushed digital service products can be determined, so that the online push corresponding to the pushed digital service products can be determined. Activity items, get a list of online push activity items of digital service products that have been pushed.

Embodiment approach F2

[0041] Embodiment F2, if the user experience feedback identification thread is a heavyweight thread, then it is determined that each feedback event in the feedback content of the product push activity corresponding to the pushed digital service product corresponds to a feature recognition degree, then the feedback containing the feature recognition degree is passed through. content, the characteristics of each item in the pushed digital service product can be determined in the product push area of ​​the pushed digital service product, so as to determine the online push activity item list of the pushed digital service product.

[0042] Further, the existing classifier / support vector machine can be used to distinguish the category data of each online push activity item. After obtaining the category data of each online push activity item, it is possible to determine the pushed digital service product according to the distribution label of each online push activity item in the push...

Embodiment approach F3

[0053] Embodiment F3: On the basis that the content of individual attention knowledge includes the characteristics of the product push region under the specified knowledge mapping space, the personalized recommendation interest knowledge base and the user experience feedback identification thread can be combined with the user experience feedback identification thread to collect the feedback content of the product push activity when specifying the product push activity feedback content. Individual attention knowledge under the knowledge mapping space, determine the product push area characteristics of several individualized interest knowledge units one by one under the specified knowledge mapping space; determine the product push area characteristics associated with several individualized interest knowledge units one by one. Concentrated product push Regional features; the regional features of product push will be centralized and determined as the product push region features of ...

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Abstract

The embodiment of the invention discloses a user big data processing method and system for personalized recommendation, and the method comprises the steps: pushing individual attention knowledge contents of personalized recommendation interest knowledge bases of audience individuals in a specified knowledge mapping space one by one through all products, and carrying out the personalized recommendation of the audience individuals in the perspective of one or more than one individual attention knowledge. The personalized recommendation interest knowledge base of the pushed digital service products is obtained, and individual attention knowledge errors of the personalized recommendation interest knowledge base of product pushing audience individuals are reduced, so that the global compatibility of the personalized recommendation interest knowledge base of the pushed digital service products can be ensured; therefore, pushing taste preferences of various product pushing audience individuals corresponding to the pushed digital service products are accurately and comprehensively recorded, and errors of interest analysis and mining are reduced, so that pertinence and efficiency of subsequent product pushing upgrading are guaranteed.

Description

technical field [0001] The present disclosure relates to the technical field of big data recommendation, and in particular, to a user big data processing method and system for personalized recommendation. Background technique [0002] Personalized recommendation technology (Recommendation technology) is the product of the development of big data and digital services. It is an advanced intelligent service technology based on massive data mining, which can provide users with personalized information services and decision support. In recent years, many very successful large-scale recommender system paradigms have emerged, and at the same time, personalized recommendation technology has gradually become one of the research focuses. At present, the development of personalized recommendation relies on user interest analysis and mining. However, with the continuous increase of user scale, how to accurately and comprehensively conduct user interest analysis to reduce the error of in...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9535G06Q30/06
CPCG06F16/9535G06Q30/0631
Inventor 黄建
Owner 黄建
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