Malicious user detection method based on feature learning and graph reasoning

A malicious user and feature learning technology, applied in the field of malicious user detection, can solve the problem that malicious user detection solutions are difficult to detect malicious user comments

Pending Publication Date: 2021-11-30
HOHAI UNIV
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Problems solved by technology

[0003] Purpose of the invention: Aiming at the problems existing in the above-mentioned background technology, the present invention provides a malicious user detection method based on feature learning and

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  • Malicious user detection method based on feature learning and graph reasoning
  • Malicious user detection method based on feature learning and graph reasoning
  • Malicious user detection method based on feature learning and graph reasoning

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Embodiment Construction

[0064] The present invention will be further described below in conjunction with the accompanying drawings.

[0065] The invention provides a malicious user detection method based on feature learning and graph reasoning, comprising the following steps:

[0066] Step S1, building basic features;

[0067] Based on the user's comment time, comment content, comment ID, commented product information and user's voting and scoring items, several features are screened out to distinguish malicious users from ordinary users. Specifically, the basic features constructed include: voting deviation, the maximum number of comments written, the average time interval, the average comment word length, the average number of votes, the number of comments, the sudden suspiciousness of comments, the extreme proportion of votes, the proportion of positive votes, and the negative votes Ratio of votes, ratio of comments appearing for the first time and the maximum number of comments a commenter makes...

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Abstract

The invention discloses a malicious user detection method based on feature learning and graph reasoning. Firstly, the suspicion degree of a product is evaluated according to a user sequence, then the suspicion degree of each user is indirectly evaluated according to the suspicion degree of the product, and then, from the product suspiciousness degree, related new features are provided, and the screened malicious users suitable for the new features are combined; and, on the other hand, the same composition hypothesis is provided, the user-user graph is constructed, feature learning of the graph neural network and a paired Markov label propagation method are fused, a unified objective function is established for iterative optimization, feature learning of graph nodes and node label reasoning are carried out, and then malicious user detection is completed.

Description

technical field [0001] The invention relates to the technical field of malicious user detection, and mainly relates to a malicious user detection method based on feature learning and graph reasoning. Background technique [0002] In recent years, a new type of malicious users has emerged on the Internet. They bypass the existing malicious user detection system through cooperation. Different from traditional malicious users, these malicious users are obviously more "smart". Specifically, , they not only comment on the target movie and TV, but also occasionally comment on the target product, which makes it look like a normal person's behavior, thereby circumventing the existing detection system of malicious users who affect consumers' behavior Decision-making, how to detect these new types of malicious users, there are usually two research ideas, one is to treat it as a single malicious user detection, and the other is to treat it as a malicious user group detection. If you r...

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Application Information

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IPC IPC(8): G06Q30/06G06K9/62G06N3/04G06N3/08G06N7/00
CPCG06Q30/0609G06N3/08G06N7/01G06N3/045G06F18/2415
Inventor 曹杰郭翔丁达陈蕾
Owner HOHAI UNIV
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