Design And Implementation Of Expert Recommendation System For Paper Review

With the rapid development of Internet and the popularization of information technology,the number of papers published is increasing day by day,while traditional selection of reviewers by manual has some drawbacks,such as inefficient processing,strong subjectivity,etc.Therefore,by studying the text representation model,this paper transforms the paper data into structured data that can be processed by computer,and combines the recommendation algorithm to recommend reviewers.In this paper,A highly efficient expert recommendation algorithm based on text clustering is proposed.Firstly,expert paper data is obtained from the network by crawler technology,and the data is screened,integrated and processed by text preprocessing and manual annotation.Secondly,the current situation of text representation model and clustering algorithm is analyzed,and a method of similarity calculation for reviewers based on text clustering is proposed,which improves the efficiency and accuracy of similarity calculation for submitting papers and publishing papers of experts.At the same time,the expert scoring model is constructed by the ability of experts' qualifications and academic influence,considering the actual situation of reviewers' selection.Results shows that the accuracy and persuasiveness of the recommendation are improved by combining the similarity and the expert scoring model.Finally,an expert system for reviewing papers is designed and implemented with the text representation model and expert recommendation algorithm proposed in this paper.Results shows the feasibility and the effectiveness of the expert recommendation system for submitting papers studied in this paper.