Method for establishing probability matrix decomposition model based on node user

A technique of probabilistic matrix decomposition and construction method, applied in the field of data mining, can solve problems such as difficulties in obtaining potential information, and achieve the effect of improving prediction accuracy

Inactive Publication Date: 2016-10-12
TIANJIN UNIV
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Bayesian models are usually trained using the Markov Monte Carlo method, and the prediction error is better than the n

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  • Method for establishing probability matrix decomposition model based on node user
  • Method for establishing probability matrix decomposition model based on node user
  • Method for establishing probability matrix decomposition model based on node user

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[0020] The technical solution of the present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0021] The present invention proposes a probability matrix decomposition model based on node users, figure 1 It is an overall schematic diagram of the present invention, including:

[0022] Step 1: Define the user's "influence", and define the user's "influence" according to the influence of the social network on the recommendation, that is, use the social relationship matrix to measure the user's "influence" in the recommendation system, let T∈{0 ,1} n*n Indicates the social relationship matrix, 1 means there is a relationship, 0 means no relationship, the more nodes with relationships, the greater the influence of the node; G=(V,E) means the network topology, n=|v| means the node number, v i represents node i, e ij represents an edge between nodes i and j.

[0023] Step 2: Formal measurement of node us...

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Abstract

The invention discloses a method for establishing a probability matrix decomposition model based on a node user. The method comprises the steps that firstly, an influencing degree of the user is defined according to influences of a social network on recommendation; secondly, formalized measurement is conducted to the influencing degree of the node user; thirdly, the probability matrix decomposition model based on the node user is established; and fourthly, the probability matrix decomposition model based on the nose user is trained. In comparison with the prior art, the method disclosed by the invention has the advantages that effects of social relations on the recommendation can be enhanced; and prediction accuracy of a recommendation algorithm can be further increased.

Description

technical field [0001] The invention belongs to a recommendation algorithm in the field of data mining, in particular to a probability matrix decomposition model based on node users. Background technique [0002] A complete recommendation system consists of three parts, including user information collection and user preference analysis, recommendation algorithm, and recommendation system implementation. The matrix factorization model is the most successful latent factor model, and it is also the most widely used model in the current recommendation system field. Its recommendation accuracy is high, and compared with the heuristic collaborative filtering algorithm, it can handle larger-scale data. At present, the existing matrix factorization models include naive probability matrix factorization model, constrained probability matrix factorization model and extended probability matrix factorization model. [0003] 1) The naive probability matrix decomposition model assumes tha...

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Application Information

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IPC IPC(8): G06F17/30G06Q50/00
CPCG06F16/9535G06Q50/01
Inventor 于瑞国黄才宝王建荣赵满坤喻梅张敏杰
Owner TIANJIN UNIV
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