Fast density clustering double-layer network recommendation method based on graph structure filtering

Abstract

The invention discloses a fast density clustering double-layer network recommendation method based on graph structure filtering. The method comprises the steps that (1) analog comment data is automatically generated through TextGAN according to historical user comment information to serve as false comments which are accurately annotated with a class mark and extremely similar to real samples; (2)historical real comments and the analog comments marked as the false comments are used as input, a graph-based virtual information filter for studying user access records is designed considering thatthe generated false comments are extremely similar to the real comments, and false users and false comments are detected through continuous iteration of confidence of users, stores and comments; and (3) in order to solve the problem of sparsity of result recommendation data, the recommendation method based on a fast density clustering double-layer network is designed. Through the method, self-adaptive selection of parameters can be realized, a good clustering result can be obtained, therefore, more effective personalized recommendation lists of users can be obtained, and the accuracy of recommendation is improved. An adversarial generative network is utilized to generate false samples extremely similar to the real comment data, and the fast density clustering double-layer network recommendation method based on graph structure filtering is efficient and reliable.

Description

technical field [0001] The invention belongs to an information recommendation method, and relates to a fast density clustering double-layer network recommendation method based on graph structure filtering. Background technique [0002] With the rapid development of network technology, information exchange is becoming more and more frequent, which brings difficulties in information selection. When users are faced with a large amount of information, they cannot obtain effective information from it, that is, the problem of information overload, and the recommendation system emerges as the times require. In actual situations, the recommendation system will affect the user's choice, and some stores will use fake users and fake reviews to increase the probability of recommending the target store and reduce the recommendation probability of other similar stores in order to maximize their personal interests. Therefore, it is very important to achieve effective filtering of fake rev...

AI Technical Summary

Problems solved by technology

[0005] In order to effectively filter the impact of false users and false information on the recommendation system, and to overcome the shortcomings of existing recommendation methods ...

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