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Recommendation method based on network local structure information filtering

A technology of local structure information and recommendation methods, applied in data exchange networks, transmission systems, instruments, etc., can solve the problems of high cost for users to obtain the required information, inability to evaluate and select, and achieve the effect of improving accuracy

Inactive Publication Date: 2018-05-22
ZHEJIANG UNIV OF TECH
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  • Claims
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Problems solved by technology

In the context of this kind of big data, the cost for users to obtain the required information is increasing, and it is no longer possible to evaluate and select these items only by relying on traditional human methods

Method used

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  • Recommendation method based on network local structure information filtering
  • Recommendation method based on network local structure information filtering
  • Recommendation method based on network local structure information filtering

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Embodiment Construction

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] refer to figure 1 , a recommendation method based on network local structure information filtering, including the following steps:

[0019] Step 1: Obtain real network structure information and establish a network model G(V, E), where V represents nodes in the network and E represents edges in the network, such as figure 1 is a schematic diagram of the local structure of a network model;

[0020] Step 2: Determine the target node v i ,Such as figure 1 The node ① in is the node v i Recommend the node with the highest possibility of connection, using θ i Indicates all possible sets of connection nodes to be recommended, such as figure 1 Node ②, node ⑤, and node ⑥ all belong to node ①’s set of recommended connection nodes θ i ;

[0021] Step 3: In the set θ i Take any node v in j ,Such as figure 1 In the node ②, get the node v i and v j The set of comm...

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Abstract

The invention discloses a recommendation method based on network local structure information filtering. The recommendation method comprises the following steps: acquiring true network structure information, and building a network model G(V,E); determining a target node v, wherein a set of all possible connection nodes to be recommended is represented with theta; optionally selecting a node v<j> from the set theta, and acquiring a common neighbor node set psi(i,j) of a node vi and the node vj; selecting a node v<h> in sequence from the set psi(i,j), calculating the degree k<h> of the node v<h>, the quantity S<ih><CN> of common neighbor nodes of the node v and the node v<h>, and the quantity S<jh><CN> of common neighbor nodes of the node v<j> and the node v<h>; calculating a similarity index of the node v<j> and the node v<j>; and calculating a similarity index between all nodes in the set theta and the node v, and selecting three nodes of which the values are greatestas three nodes of which the connection probability is highest. Through adoption of the recommendation method, the link prediction algorithm accuracy can be increased effectively.

Description

technical field [0001] The invention relates to the technical field of network recommendation, in particular to a recommendation method based on network local structure information filtering. Background technique [0002] The rapid development of computers, the Internet and web technology has changed people's lives. People make friends in virtual communities, browse news on news websites, watch movies on video websites, consult books in virtual libraries, and browse books on e-commerce platforms. Purchase items. However, while enjoying a colorful life, people also feel the annoyance brought by information expansion, that is, people cannot quickly and effectively find the most relevant information in massive amounts of data. The data volume of movies, books, web pages and other information is tens of millions, and the growth rate of these data information has far exceeded the natural processing ability of human beings. In the context of this kind of big data, the cost for u...

Claims

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Application Information

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IPC IPC(8): H04L29/08H04L12/24H04L29/06G06F17/30
CPCH04L41/145H04L41/147H04L63/0227G06F16/9535H04L67/535
Inventor 杨旭华徐恩平
Owner ZHEJIANG UNIV OF TECH
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