Abstract
In view of the increasing number of existing papers, this paper is a study of paper recommendation system. The data set used in this paper is the DBLP citation network in AMiner. First of all, we build a three layers citation network graph model. In this model, we integrate the citation relationship, paper’s feature information, co-authorship relationship and research field information into this model. Secondly, we proposed the algorithm PAFRWR. This algorithm combines three layers citation network graph mode with RWR. And, the search vector is constructed by word2vec model. Finally, in this experiment, using Recall@N and NDCG@N as evaluation metric. Then the restart probability of PAFRWR is determined by experiments. And the most effective search vector is determined by comparison. The Recall@N and NDCG@N of PAFRWR are higher than PageRank, LDA and Link-PLSA-LDA through the experiment. So the recommendation model and algorithm in this paper are more accurate and effective.
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Acknowledgments
This work was supported by the project is the Yunnan Provincial Smart Education Key Laboratory Project,Key Laboratory of Education Informalization for Nationalities of Ministry of Education and the Yunnan University Innovation Research Team Project.
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Jing, S., Yu, S. (2020). Research of Paper Recommendation System Based on Citation Network Model. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12488. Springer, Cham. https://doi.org/10.1007/978-3-030-62463-7_22
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