Fraudulent conduct identification system based on machine learning in classified information website

A technology for classifying information and machine learning, applied in the Internet field to improve authenticity and reduce falsehoods

Active Publication Date: 2014-05-14
BEIJING 58 INFORMATION TECH
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  • Abstract
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Problems solved by technology

In the existing methods, the identification of rules can only be distinguished by using linear classificat

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  • Fraudulent conduct identification system based on machine learning in classified information website

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Embodiment Construction

[0021] The objects and functions of the present invention and methods for achieving the objects and functions will be clarified by referring to the exemplary embodiments. However, the present invention is not limited to the exemplary embodiments disclosed below; it can be implemented in various forms. The essence of the description is only to help those skilled in the relevant art comprehensively understand the specific details of the present invention.

[0022] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0023] The fraudulent information identification method of the present invention uses the data generated based on the user's behavior, and can identify the information data released by the user in real time. The model recognition of machine learning adopted by the present invention can rec...

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Abstract

The invention provides a method used for a fraudulent conduct identification system based on machine learning in a classified information website. The method includes the following steps that (a), sample data are extracted based on existing user behavior data and used for generating a model for the first time; (b), multiple user behavior characteristics are selected to be extracted according to training data of different service types; (c), based on the extracted user behavior characteristics, the sample training data are vectorized; (d), the vectorized sample training data are used for generating a prediction model; (e), on-line data are detected by using the generated model based on classification and cluster rules; (f), detected abnormal user data are processed. User behaviors can be identified in multiple dimensions through the method, and the false amount of trade information can be reduced efficiently. Moreover, the user behaviors of low quality can be identified well even if the training data contain noise data.

Description

technical field [0001] The invention relates to Internet technology, in particular to a machine learning-based fraud identification system in a classified information website. Background technique [0002] Classified information network is a newly emerging type of website on the Internet that involves all aspects of daily life. In these websites, users can obtain free and convenient information publishing services, including second-hand goods transactions, second-hand car sales, house rental and sales, pets, recruitment, part-time jobs, job hunting, dating activities, life service information, etc. Classified information is also called classified advertisements. The advertisements that people see on TV and newspapers are often imposed on the viewers whether they are willing or not. This type of advertisement is a passive advertisement; while people actively inquire about recruitment, Information on renting, traveling, etc., for this information, it is called active advertis...

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Application Information

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IPC IPC(8): G06F17/30G06F15/18
CPCG06F16/958
Inventor 张鹏张爱华张美琦张朝阳孙亚健
Owner BEIJING 58 INFORMATION TECH
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