1 Tsafnat G, Glasziou P, Choong M K, et al. Systematic review automation technologies[J]. Systematic Reviews, 2014, 3: Article No.74. 2 Wang J, Zhang C Z, Zhang M Y, et al. CitationAS: a tool of automatic survey generation based on citation content[J]. Journal of Data and Information Science, 2018, 3(2): 20-37. 3 Portenoy J, West J D. Constructing and evaluating automated literature review systems[J]. Scientometrics, 2020, 125(3): 3233-3251. 4 Wang L L, Lo K. Text mining approaches for dealing with the rapidly expanding literature on COVID-19[J]. Briefings in Bioinformatics, 2021, 22(2): 781-799. 5 Portenoy J, West J D. Supervised learning for automated literature review[C]// Proceedings of the 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries. CEUR-WS.org, 2019: 83-91. 6 Nye B E, Nenkova A, Marshall I J, et al. Trialstreamer: mapping and browsing medical evidence in real-time[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Stroudsburg: Association for Computational Linguistics, 2020: 63-69. 7 Tang J, Jin R M, Zhang J. A topic modeling approach and its integration into the random walk framework for academic search[C]// Proceedings of the 2008 Eighth IEEE International Conference on Data Mining. IEEE, 2008: 1055-1060. 8 Sayyadi H, Getoor L. FutureRank: ranking scientific articles by predicting their future PageRank[C]// Proceedings of the 2009 SIAM International Conference on Data Mining. Philadelphia: Society for Industrial and Applied Mathematics, 2009: 533-544. 9 Wang Y J, Tong Y H, Zeng M. Ranking scientific articles by exploiting citations, authors, journals, and time information[C]// Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2013: 933-939. 10 Xiong C Y, Power R, Callan J. Explicit semantic ranking for academic search via knowledge graph embedding[C]// Proceedings of the 26th International Conference on World Wide Web. Republic and Canton of Geneva: International World Wide Web Conferences Steering Committee, 2017: 1271-1279. 11 Gargiulo F, Silvestri S, Fontanella M, et al. A deep learning approach for scientific paper semantic ranking[C]// Proceedings of the International Conference on Intelligent Interactive Multimedia Systems and Services. Cham: Springer, 2018: 471-481. 12 黄永, 陆伟, 程齐凯, 等. 学术文本的结构功能识别——在学术搜索中的应用[J]. 情报学报, 2016, 35(4): 425-431. 13 王瑞雪, 方婧, 李信, 等. 学术查询意图类目体系构建与分析:百度学术查询日志的实证[J]. 图书情报工作, 2021, 65(4): 73-80. 14 万连城. 面向问题导向的学术文献搜索引擎研究[J]. 电子科技, 2016, 29(12): 142-144, 147. 15 Balabanovic M, Shoham Y. Fab: content-based, collaborative recommendation[J]. Communications of the ACM, 1997, 40(3): 66-72. 16 李响, 谭静. 融合相关性与多样性的学术论文推荐方法研究[J]. 情报理论与实践, 2017, 40(6): 99-103. 17 谭红叶, 要一璐, 梁颖红. 基于知识脉络的科技论文推荐[J]. 山东大学学报(理学版), 2016, 51(5): 94-101. 18 杨凯, 王利, 周志平, 等. 基于内容和协同过滤的科技文献个性化推荐[J]. 信息技术, 2019, 43(12): 11-14. 19 Asabere N Y, Xia F, Meng Q X, et al. Scholarly paper recommendation based on social awareness and folksonomy[J]. International Journal of Parallel, Emergent and Distributed Systems, 2015, 30(3): 211-232. 20 Vellino A. Recommending research articles using citation data[J]. Library Hi Tech, 2015, 33(4): 597-609. 21 Zhou Q, Chen X Z, Chen C S. Authoritative scholarly paper recommendation based on paper communities[C]// Proceedings of the 2014 IEEE 17th International Conference on Computational Science and Engineering. IEEE, 2014: 1536-1540. 22 Gori M, Pucci A. Research paper recommender systems: a random-walk based approach[C]// Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence. IEEE, 2006: 778-781. 23 Bai X M, Wang M Y, Lee I, et al. Scientific paper recommendation: a survey[J]. IEEE Access, 2019, 7: 9324-9339. 24 步一, 许家伟, 黄文彬. 基于引文的科学文献定量评价: 引文影响力指标评述[J]. 图书情报知识, 2021, 38(6): 47-59, 46. 25 Aksnes D W, Langfeldt L, Wouters P. Citations, citation indicators, and research quality: an overview of basic concepts and theories[J]. SAGE Open, 2019, 9(1). DOI: 10.1177/2158244019829575. 26 Merton R K. Priorities in scientific discovery: a chapter in the sociology of science[J]. American Sociological Review, 1957, 22(6): 635-659. 27 Gilbert G N. Referencing as persuasion[J]. Social Studies of Science, 1977, 7(1): 113-122. 28 Garfield E. Can citation indexing be automated?[C]// Symposium Proceedings of Statistical Association Methods for Mechanized Documentation, 1965, 269: 189-192. 29 Lyu D Q, Ruan X M, Xie J, et al. The classification of citing motivations: a meta-synthesis[J]. Scientometrics, 2021, 126(4): 3243-3264. 30 马凤, 武夷山. 关于论文引用动机的问卷调查研究——以中国期刊研究界和情报学界为例[J]. 情报杂志, 2009, 28(6): 9-14, 8. 31 邱均平, 陈晓宇, 何文静. 科研人员论文引用动机及相互影响关系研究[J]. 图书情报工作, 2015, 59(9): 36-44. 32 Tahamtan I, Afshar A S, Ahamdzadeh K. Factors affecting number of citations: a comprehensive review of the literature[J]. Scientometrics, 2016, 107(3): 1195-1225. 33 Belter C W. Citation analysis as a literature search method for systematic reviews[J]. Journal of the Association for Information Science and Technology, 2016, 67(11): 2766-2777. 34 Janssens A C J W, Gwinn M. Novel citation-based search method for scientific literature: application to meta-analyses[J]. BMC Medical Research Methodology, 2015, 15(1): 84. 35 Chen T T. The development and empirical study of a literature review aiding system[J]. Scientometrics, 2012, 92(1): 105-116. 36 Yu T, Yu G, Li P Y, et al. Citation impact prediction for scientific papers using stepwise regression analysis[J]. Scientometrics, 2014, 101(2): 1233-1252. 37 Peng T Q, Zhu J J H. Where you publish matters most: a multilevel analysis of factors affecting citations of internet studies[J]. Journal of the American Society for Information Science and Technology, 2012, 63(9): 1789-1803. 38 Yan R, Tang J, Liu X B, et al. Citation count prediction: learning to estimate future citations for literature[C]// Proceedings of the 20th ACM International Conference on Information and Knowledge Management. New York: ACM Press, 2011: 1247-1252. 39 Dong Y X, Johnson R A, Chawla N V. Will this paper increase your h-index? Scientific impact prediction[C]// Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. New York: ACM Press, 2015: 149-158. 40 Uzzi B, Mukherjee S, Stringer M, et al. Atypical combinations and scientific impact[J]. Science, 2013, 342(6157): 468-472. 41 Roth C, Wu J, Lozano S. Assessing impact and quality from local dynamics of citation networks[J]. Journal of Informetrics, 2012, 6(1): 111-120. 42 Yan R, Huang C R, Tang J, et al. To better stand on the shoulder of giants[C]// Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries. New York: ACM Press, 2012: 51-60. 43 Bai X M, Zhang F L, Lee I. Predicting the citations of scholarly paper[J]. Journal of Informetrics, 2019, 13(1): 407-418. 44 Chakraborty T, Kumar S, Goyal P, et al. Towards a stratified learning approach to predict future citation counts[C]// Proceedings of the IEEE/ACM Joint Conference on Digital Libraries. IEEE, 2014: 351-360. 45 Zhang X Y, Xie Q, Song M. Measuring the impact of novelty, bibliometric, and academic-network factors on citation count using a neural network[J]. Journal of Informetrics, 2021, 15(2): 101140. 46 Liu T Y. Learning to rank for information retrieval[J]. Foundations and Trends in Information Retrieval, 2009, 3(3): 225-331. 47 Fan R E, Chang K W, Hsieh C J, et al. LIBLINEAR: a library for large linear classification[J]. Journal of Machine Learning Research, 2008, 9: 1871-1874. 48 Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32. 49 Zhu J, Zou H, Rosset S, et al. Multi-class AdaBoost[J]. Statistics and Its Interface, 2009, 2(3): 349-360. 50 Geurts P, Ernst D, Wehenkel L. Extremely randomized trees[J]. Machine Learning, 2006, 63(1): 3-42. 51 Friedman J H. Greedy function approximation: a gradient boosting machine[J]. The Annals of Statistics, 2001, 29(5): 1189-1232. 52 Burges C J C. From RankNet to LambdaRank to LambdaMART: an overview[R]. Microsoft Research Technical Report, 2010: MSR-TR-2010-82. 53 Burges C J C, Ragno R, Le Q V. Learning to rank with nonsmooth cost functions[C]// Proceedings of the 19th International Conference on Neural Information Processing Systems. Cambridge: The MIT Press, 2006: 193-200. 54 ?trumbelj E, Kononenko I. Explaining prediction models and individual predictions with feature contributions[J]. Knowledge and Information Systems, 2014, 41(3): 647-665. 责任编辑 潘尧