Academic paper recommendation method based on network representation and auxiliary information embedding

Abstract

The invention discloses an academic paper recommendation method based on network representation and auxiliary information embedding, and the method comprises the following steps: 1, constructing a citation network, carrying out the dimensionality reduction of variables with significant influence in each paper through principal component analysis, obtaining a paper edge weight composed of multiple factors, adding paper edge weights on the basis of the citation network, and constructing a paper influence network; 2, generating a paper sequence in the paper influence network, learning an embedding vector of the paper sequence by an algorithm to obtain a primary graph embedding function of the paper, and adding auxiliary information of the paper into the primary graph embedding function to obtain an ultimate graph embedding function of the paper; 3, taking the ultimate diagram embedding function of the papers as an embedding model, calculating the similarity between each paper and the papers in which the user is interested, and generating a recommendation list. The method has the advantages that the accuracy of the paper recommendation result is high, and the cold start problem of the paper is relieved to a certain extent.

Description

technical field [0001] The invention relates to an academic paper recommendation method, in particular to an academic paper recommendation method based on network representation and auxiliary information embedding. Background technique [0002] With the continuous development of the network and the continuous increase of the amount of data on the network, the way users obtain information is also changing. As an effective information filtering method, the recommendation system can effectively alleviate the problem of information explosion. In academic paper recommendation, most of the algorithms related to paper recommendation are based on network representation models. The power-law distribution characteristics show that most nodes in the network are associated with a small number of edges. Therefore, for nodes with limited information, it is difficult to find effective display method. [0003] In order to improve the embedding degree of a few nodes, many scholars have adop...

AI Technical Summary

Problems solved by technology

In academic paper recommendation, most of the algorithms related to paper recommendation are based on the network representation model, and the power-law distribution characteristics indicate that most nodes in the network are associated with a small number of edges. Therefore, for nodes with limited information, it is difficult to find effective display method

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