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A bipartite graph-based random walk recommendation method

A technology of random walk and recommendation method, which is used in sales/rental transactions, instruments, computing, etc. It can solve the problem of sparse recommendation methods, and achieve the effect of reducing adverse effects, increasing the probability of walking, and improving efficiency.

Active Publication Date: 2022-07-26
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

The method of the present invention regards the recommendation problem as a semi-supervised multi-label classification problem, which can solve the problem of sparsity in the recommendation method, making the recommendation result more in line with the needs of practical applications

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  • A bipartite graph-based random walk recommendation method
  • A bipartite graph-based random walk recommendation method
  • A bipartite graph-based random walk recommendation method

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Embodiment Construction

[0036] The present invention will be further described below with reference to the accompanying drawings and embodiments, and the present invention includes but is not limited to the following embodiments.

[0037] The present invention provides a random walk recommendation method based on bipartite graph, and its basic realization process is as follows: figure 1 shown. The method of the present invention mainly adopts the idea of ​​random walk, such as figure 2 As shown, the user and the product are regarded as two node sets of the bipartite graph respectively, and the score value is used as the weight between the nodes. Starting from the target user node, the walker walks on the bipartite graph. At each step, the walker can randomly walk to the next node connected to the current node. The weight represents the user's preference for the product. The more a user likes a certain product, the higher the weight value between them. Therefore, when the walker selects the next n...

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Abstract

The invention provides a random walk recommendation method based on bipartite graph. First, construct a user-product bipartite graph, that is, construct a similarity matrix according to the scoring matrix, and perform Laplace regularization; The transition matrix of the condition; finally, the recommendation matrix is ​​obtained by calculation, and the recommendation matrix is ​​sorted in descending order to obtain the recommendation result. The method of the invention regards the recommendation problem as a semi-supervised multi-label classification problem, and adopts the idea of ​​random walk, which can improve the recommendation accuracy rate of sparse data and make the recommendation result more in line with the requirements of practical applications.

Description

technical field [0001] The invention belongs to the technical field of collaborative filtering recommendation systems, and in particular relates to a bipartite graph-based random walk recommendation method. Background technique [0002] With the popularity and rapid development of the Internet, information has grown exponentially. When faced with a large amount of information, users cannot easily obtain the information they need, which leads to a reduction in the utilization rate of information. This phenomenon is information overload. At present, many search engines and portal websites use information retrieval technology to solve the problem of information overload, and they help users obtain useful information from a large amount of network information. However, when users use the same keywords to search for information, the results they get are consistent, which leads to a low degree of personalization of the retrieved information, which cannot meet the interests of use...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9535G06Q30/06
Inventor 聂飞平裴朝于为中王榕李学龙
Owner NORTHWESTERN POLYTECHNICAL UNIV
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