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Information recommendation method and system considering compactness of social network user group

A technology for social network and information recommendation, applied in the field of social network mining, can solve the problems of high time consumption, large breadth-first traversal strategy search range, and difficulty in sharing and utilization of calculation results, so as to save costs and improve experience.

Pending Publication Date: 2021-01-15
YANTAI UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] (2) The entire calculation process is executed sequentially. When the number of vertices in the set V is large, it takes a lot of time
[0008] (3) When traversing the graph starting from each vertex in the set S, there are a lot of repeated calculations, and it is difficult to realize the shared utilization of existing calculation results
[0009] (4) Although some existing work uses a bidirectional breadth-first traversal strategy to calculate the shortest route between two points, it needs to maintain a large number of routes and the search range of the breadth-first traversal strategy is too large, resulting in low query efficiency
[0010] (5) In the process of information recommendation in the prior art, the closeness of user group relationship is not considered, and it is meaningless and valuable to recommend information or products between users in a group that is not close enough, and even waste the cost of the merchant
[0011] To sum up, in the prior art, how to design an efficient parallel computing strategy to improve the computing efficiency of the shortest distance between multiple points on a very large graph still lacks an effective solution

Method used

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  • Information recommendation method and system considering compactness of social network user group
  • Information recommendation method and system considering compactness of social network user group
  • Information recommendation method and system considering compactness of social network user group

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0043] This embodiment provides an information recommendation method that considers the closeness of social network user groups;

[0044] An information recommendation method that considers the closeness of social network user groups, including:

[0045] S101: Obtain all users in the user group to be analyzed in the social network;

[0046] S102: Construct a graph structure according to all users in the user group to be analyzed; that is, each user is regarded as a vertex of the graph structure, and if there is a friend relationship or follow relationship between users, it means There are edges corresponding to the connection, and the number of interactions between users is regarded as the weight of the edge;

[0047] S103: Obtain a vertex set based on the graph structure, and query the shortest distance between any two vertices in the vertex set;

[0048]S104: weighted and summed the shortest distance between any two vertices to obtain the relationship closeness of the user...

Embodiment 2

[0127] This embodiment provides an information recommendation system that considers the closeness of social network user groups;

[0128] An information recommendation system that considers the closeness of social network user groups, including:

[0129] An acquisition module configured to: acquire all users in the user group to be analyzed in the social network;

[0130] The graph structure component module is configured to: construct a graph structure according to all users in the user group to be analyzed; that is, each user is regarded as a vertex of the graph structure, if there is a friend relationship or a follow relationship between users, then Indicates that there is a correspondingly connected edge between the vertices corresponding to the user, and the number of interactions between users is regarded as the weight of the edge;

[0131] A query module configured to: obtain a vertex set based on a graph structure, and query the shortest distance between any two verti...

Embodiment 3

[0138] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are programmed Stored in the memory, when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in Embodiment 1 above.

[0139] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, o...

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Abstract

The invention discloses an information recommendation method and system considering the compactness of a social network user group. The method comprises the steps of obtaining all users in a to-be-analyzed user group in a social network; constructing a graph structure according to all users in the to-be-analyzed user group; regarding each user as a vertex of the graph structure, if friend relationships or attention relationships exist among the users, indicating that correspondingly connected edges exist among the vertexes corresponding to the users, and regarding the interaction times among the users as the weights of the edges; obtaining a vertex set based on the graph structure, and querying the shortest distance between any two vertexes in the vertex set; performing weighted summationon the shortest distance between any two vertexes to obtain the relationship closeness of the to-be-analyzed user group; judging whether the relationship closeness of the to-be-analyzed user group isgreater than a set threshold value or not, and if so, performing information recommendation on each user in the to-be-analyzed user group, namely recommending commodities or services preferred by other users in the to-be-analyzed user group to the current user; otherwise, not recommending the information.

Description

technical field [0001] The present application relates to the technical field of social network mining, in particular to an information recommendation method and system considering the closeness of social network user groups. Background technique [0002] The statements in this section merely mention the background art related to this application, and do not necessarily constitute the prior art. [0003] The problem of calculating the shortest distance between multiple points based on a graph is to give a directed or undirected non-negative weighted graph G, select a set S of any number of points on the graph G, and calculate the distance between any two points in the set S. The shortest distance on G. The shortest distance between two points is the sum of the weights of all edges on the shortest path between these two points. In reality, the data models of several applications can be abstracted as directed or undirected graphs. A social network can be abstracted into a g...

Claims

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Application Information

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IPC IPC(8): G06F16/9536G06F16/9535
CPCG06F16/9536G06F16/9535Y02D10/00
Inventor 于自强禹晓辉戴天伦童向荣徐金东王莹洁
Owner YANTAI UNIV
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