Golden citation algorithm-based paper recommendation method

A recommendation method and paper technology, applied in the field of paper recommendation based on the Golden Citation Algorithm, can solve problems such as not fully reflecting the quality of papers

Active Publication Date: 2018-11-06
北京市科学技术情报研究所
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Problems solved by technology

The application only considers the similarity of citations between the papers or the similarity of the content of the papers, but it does not fully reflect the quality of the papers

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  • Golden citation algorithm-based paper recommendation method
  • Golden citation algorithm-based paper recommendation method
  • Golden citation algorithm-based paper recommendation method

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Embodiment 1

[0065] The embodiment of the present invention introduces a paper sorting method based on the golden citation algorithm, which includes the following steps: First, select the paper group range in the paper database;

[0066] Secondly, build an initial citation network model by using the mutual citation relationship of papers in the selected paper group;

[0067] Then, calibrate the citation attributes of the paper in the initial citation network model, and introduce the self-citation coefficient of the paper to revise the number of citations to generate a group of self-cited revised papers;

[0068] Finally, an iterative screening method is used to narrow down the scope of self-cited revised paper groups, and the papers selected from each level are grouped into paper groups and arranged in descending order.

[0069] In this embodiment, the group of papers may be defined according to the subject, field, discipline, and / or age, or defined according to a set search strategy, or all papers...

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Abstract

The invention discloses a golden citation algorithm-based paper recommendation method. The method comprises the following steps of: firstly, selecting a paper group range in a paper database; secondly, constructing an initial citation network model according to mutual citation relationships between papers in the selected paper group; thirdly, demarcating citation attributes of papers in the initial citation network model, and revising a citation frequency through a self-citation coefficient so as to generate a self-citation revising paper group; and finally, iteratively narrowing the range ofthe self-citation revising paper group until a result is converged, so as to obtain a golden citation paper. Through demarcating citation attributes of papers, interferences of self-citation in the paper citation relationships are eliminated; and paper groups are recursively narrowed according to self-citation revising citation frequencies, so that the interferences of low-quality and low-efficiency cited papers are eliminated, and users can rapidly and correctly retrieve high-quality papers.

Description

Technical field [0001] The invention belongs to the technical field of big data paper retrieval recommendation, and specifically relates to a paper recommendation method based on a golden citation algorithm. Background technique [0002] Based on the difference of recommendation information, paper recommendation is mainly divided into content-based recommendation, collaborative filtering-based recommendation, social recommendation and hybrid model recommendation. In the paper recommendation system, there is still a special citation-based recommendation, but it is a very difficult task to obtain high-quality scientific and technological papers from a large number of good and bad literature materials. How to automatically evaluate effectively The quality of scientific papers has become a research topic. The number of citations is an objective and intuitive indicator to measure the quality of a paper. Paper citations include the author’s self-citation in subsequent papers and other...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 吴晨生杜丽萍李梦辉刘静黄玉荣
Owner 北京市科学技术情报研究所
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