News recommendation method and system based on user behavior detection and computer equipment

A behavioral and user-based technology, applied in the field of data analysis, can solve problems such as insufficient optimization capabilities and ignoring the production environment, and achieve the effects of speeding up mining, reducing running time, and avoiding useless calculations
CN110489652AActive Publication Date: 2019-11-22CHONGQING UNIV OF POSTS & TELECOMM
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHONGQING UNIV OF POSTS & TELECOMM
Publication Date
2019-11-22

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Abstract

The invention relates to a news recommendation method and system based on user behavior data detection and computer equipment. The method comprises the following steps of: converting the news click behavior data of the user; generating a processible user behavior data set; filtering non-frequent behaviors in the user behavior data set; obtaining a relative frequent item set by using a frequent item set serial mining algorithm; constructing a relatively frequent item set tree formed by a relatively frequent item set in the user behavior, finding out an optimal frequent item set; calculating thecomplete support degree of the optimal frequent item set in the user behaviors, generating association rules related to the user behavior frequent item set according to the complete support degree, and recommending news to users according to the association rules. The real production environment can be fully considered, a large number of user behavior data sets can be efficiently processed, and the method belongs to the technical field of data analysis.
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Description

technical field

[0001] The present invention relates to the technical field of data analysis, in particular to a news recommendation method, system and computer equipment based on user behavior data detection. Background technique

[0002] News recommendation algorithm is a hot spot in new media research at present. In the early days of Internet development, popular recommendation methods for information were developed and widely used in websites such as Yahoo. In the current era of big data, personalized algorithm recommendation and information flow are the mainstream. With the development of communication technology and mobile Internet of Things, the scale of data is becoming larger and larger. Data mining can extract important information from large-scale data, and it is widely used in various fields, such as user behavior analysis, spatial prediction and network security detection. However, classical algorithms require a large amount of calculations for massive data, w...

Claims

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