Abstract
The explosive growth of the number of papers leads to the over-expansion of the paper information, resulting in the problem of information overload, causing a lot of trouble for researchers to find the papers they need. This paper implements a paper recommendation system based on LDA and PageRank. In this system, we recommend papers in the same field to researchers by modeling and analyzing paper data. Therefore, we can provide researchers with a quick and effective reference for related papers in the research field. In this paper, the probability distribution calculation and keywords extraction of paper topics and words are carried out according to the LDA of papers in the paper pool. At the same time, word2dec is used to represent the topic vector, doc2vec is used to represent the paper vector, and by calculating the system similarity between documents and topics, we get the top N papers that are most similar to the topic input by users. We use the PageRank algorithm to reorder the options in the reference network to get the final recommendation results. Under the experimental data set, the accuracy of system recommendation is more than 60%, which has high reliability and achieves the corresponding recommendation effect.
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Acknowledgments
This work is supported in part by National Natural Science Foundation of China (61728204), Innovation Funding (NJ20160028, NT2018027, NT2018028, NS2018057), Aeronautical Science Foundation of China (2016551500), State Key Laboratory for smart grid protection and operation control Foundation, Association of Chinese Graduate Education (ACGE).
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Tao, M., Yang, X., Gu, G., Li, B. (2020). Paper Recommend Based on LDA and PageRank. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1254. Springer, Singapore. https://doi.org/10.1007/978-981-15-8101-4_51
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DOI: https://doi.org/10.1007/978-981-15-8101-4_51
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