Personalized recommendation method based on community discovery

A recommendation method and community discovery technology, applied in the field of personalized recommendation based on community discovery, can solve problems such as label filtering of similar neighbor nodes, and achieve the effect of improving accuracy and increasing stickiness

Active Publication Date: 2020-06-12
BEIJING UNIV OF TECH
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Problems solved by technology

[0007] Although the existing label propagation algorithm has achieved certain results in community detection for over...

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  • Personalized recommendation method based on community discovery
  • Personalized recommendation method based on community discovery
  • Personalized recommendation method based on community discovery

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Embodiment Construction

[0055] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0056] Below in conjunction with accompanying drawing, the present invention is described in further detail:

[0057] Community structure is a common feature in complex networks, and the entire network is composed of many communities. The connection between nodes in the same community is very tight, while the connection between communities i...

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Abstract

The invention discloses a personalized recommendation method based on community discovery, and the method comprises the steps: giving a unique label to each node, and calculating the degree of each node; calculating node weights according to the degrees of the nodes; calculating structural similarity and user preference similarity of the nodes; calculating the similarity of the nodes according tothe structural similarity of the nodes and the user preference similarity; calculating the propagation probability of each label according to the node similarity and the node weight; deleting the labels of which the propagation probabilities are smaller than a threshold value, and taking the remaining labels as community division results; and friend recommendation in the community is carried out on the generated community by using link prediction. According to the method, community discovery is carried out on all nodes by using an improved label propagation algorithm, and then link predictionis carried out according to a community discovery result; the accuracy of social network friend recommendation is improved, and the stickiness of a user is improved.

Description

technical field [0001] The invention relates to the technical field of data mining in complex networks, in particular to a personalized recommendation method based on community discovery. Background technique [0002] With the development of information technology and the Internet, people have gradually entered the era of information overload. In social networks, due to the surge in the number of various types of social media users, it is difficult to find information they are interested in. Therefore, the friend recommendation service on social platforms, as an important means of information filtering, can recommend users with the same or similar interests to users through community division without the explicit request of users. A high-quality recommendation model will make users depend on the system, not only increasing user stickiness, but also establishing a long-term stable relationship. [0003] Complex network is an abstraction of complex system. In reality, many c...

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Application Information

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IPC IPC(8): G06F16/9535G06F16/9536
CPCG06F16/9535G06F16/9536Y02D10/00
Inventor 何泾沙曹珊朱娜斐滕达
Owner BEIJING UNIV OF TECH
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