| INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY |
Prev
Next
|
|
|
A multilayer network diffusion-based model for reviewer recommendation |
| Yiwei Huang(黄羿炜)1,2, Shuqi Xu(徐舒琪)3, Shimin Cai(蔡世民)4,5,†, and Linyuan Lü(吕琳媛)6,1,2,‡ |
1 Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China;
2 Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China;
3 Institute of Dataspace, Hefei Comprehensive National Science Center, Hefei 230088, China;
4 Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China;
5 School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China;
6 School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China |
|
|
|
|
Abstract With the rapid growth of manuscript submissions, finding eligible reviewers for every submission has become a heavy task. Recommender systems are powerful tools developed in computer science and information science to deal with this problem. However, most existing approaches resort to text mining techniques to match manuscripts with potential reviewers, which require high-quality textual information to perform well. In this paper, we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network, with no requirement for textual information. The network incorporates the relationship of scholar-paper pairs, the collaboration among scholars, and the bibliographic coupling among papers. Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing, with improvements of over 7.62% in recall, 5.66% in hit rate, and 47.53% in ranking score. Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem, which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.
|
Received: 24 September 2023
Revised: 22 December 2023
Accepted manuscript online: 22 December 2023
|
|
PACS:
|
89.75.Hc
|
(Networks and genealogical trees)
|
| |
64.60.aq
|
(Networks)
|
|
| Fund: Project supported by the National Natural Science Foundation of China (Grant No. T2293771) and the New Cornerstone Science Foundation through the XPLORER PRIZE. |
Corresponding Authors:
Shimin Cai, Linyuan Lü
E-mail: shimin.cai81@gmail.com;linyuan.lv@ustc.edu.cn
|
Cite this article:
Yiwei Huang(黄羿炜), Shuqi Xu(徐舒琪), Shimin Cai(蔡世民), and Linyuan Lü(吕琳媛) A multilayer network diffusion-based model for reviewer recommendation 2024 Chin. Phys. B 33 038901
|
[1] Patil A H and Mahalle P N 2020 Procedia Comput. Sci. 171 709
[2] Zhao X Q and Zhang Y S 2022 Inf. Process. Manag. 59 103028
[3] Zaharie M A and Seeber M 2018 Scientometrics 117 1587
[4] Duan Z, Tan S C, Zhao S, Wang Q Q, Chen J and Zhang Y P 2019 Neurocomputing 366 97
[5] Liu X, Suel T and Memon N 2014 Proceedings of the 8th ACM Conference on Recommender Systems, October 6-10, 2014, Foster City, USA, p. 25
[6] Di Mauro N, Basile T M and Ferilli S 200518th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, June 22-24, 2005, Bari, Italy, p.~789
[7] Tan S C, Duan Z, Zhao S, Chen J and Zhang Y P 2021Inform. Retrieval J. 24 175
[8] Yang J H, Chen C M, Wang C J and Tsai M F 2018Proceedings of the 12th ACM Conference on Recommender Systems, October 2-7, 2018, Vancouver, Canada, p.~140
[9] Dumais S T and Nielsen J 1992Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, June 21-24, 1992, Copenhagen, Denmark, p. 233
[10] Tayal D K, Saxena P C, Sharma A, Khanna G and Gupta S 2014Appl. Intell. 40 54
[11] Kou N M, Leong Hou U, Mamoulis N and Gong Z 2015Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, May 31-June 4, 2015, Melbourne, Australia, p.~2031
[12] Zhao S, Zhang D, Duan Z, Chen J, Zhang Y P and Tang J 2018Scientometrics 115 1293
[13] Yarowsky D and Florian R 1999Proceedings of the 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, June 21-22, 1999, Maryland, USA, p.~220
[14] Mirzaei M, Sander J and Stroulia E 2019Inf. Process. Manag. 56 858
[15] Yang C, Liu T T, Yi W J, Chen X H and Niu B 2020Appl. Soft Comput. 94 106483
[16] Zhang D, Zhao S, Duan Z, Chen J, Zhang Y P and Tang J 2020ACM Trans. Inf. Syst. 38 5
[17] Kalmukov Y 2020Proceedings of the 21st International Conference on Computer Systems and Technologies, June 19-20, 2020, Ruse, Bulgaria, p.~229
[18] Abduljaleel A Q, Naser M A and Al-mamory S O 2021Turk. J. Comput. Math. Educ. 12 619
[19] Kalmukov Y 2012Comput. Sci. Inf. Syst. 9 763
[20] Protasiewicz J, Pedrycz W, Kozlowski M, Dadas S, Stanislawek T, Kopacz A and Galȩżewska M 2016Knowl. Based Syst. 106 164
[21] Blei D M, Ng A Y and Jordan M I 2003J. Mach. Learn. Res. 3 993
[22] Mimno D and McCallum A 2007 Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 12-15, 2007, San Jose, USA, p.~500
[23] Rosen-Zvi M, Griffiths T L, Steyvers M and Smyth P 2004 Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence, July 7-11, 2004, Banff, Canada, p.~487
[24] Jin J, Geng Q, Mou H K and Chen C 2019 J. Inf. Sci. 45 554
[25] Peng H W, Hu H J, Wang K Q and Wang X L 2017 International Conference on Database Systems for Advanced Applications, March 27-30, 2017, Suzhou, China, p.~145
[26] Ogunleye O, Ifebanjo T, Abiodun T and Adebiyi A 2017 Covenant University Conference on E-Governance in Nigeria, 2017, Ota, Nigeria, p.~211
[27] Yin H Z, Cui B, Lu H and Zhao L 2016 2016 Conference on Technologies and Applications of Artificial Intelligence, November 25-27, 2016, Hsinchu, China, p.~1
[28] Jin J, Niu B Z, Ji P and Geng Q 2020 Ann. Oper. Res. 291 409
[29] Nie L, Davison B D and Qi X G 2006 Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 6-11, 2006, Seattle, USA, p.~91
[30] Rodriguez M A and Bollen J 2008 Proceedings of the 17th ACM Conference on Information and Knowledge Management, October 26-30, 2008, Napa Valley, USA, p. 319
[31] Goldsmith J and Sloan R H 2007 Proceedings of AAAI Workshop on Preference Handling for Artificial Intelligence, July 22-23, 2007, Vancouver, Canada, p.~53
[32] Tong H H, Faloutsos C and Pan J Y 2006 Proceedings of the Sixth International Conference on Data Mining, December 18-22, 2006, Hong Kong, China, p.~613
[33] Xu Y H and Du Y W 2013 2013 Sixth International Conference on Business Intelligence and Financial Engineering, November 14-16, 2013, Hangzhou, China, p. 552
[34] Pradhan T, Sahoo S, Singh U and Pal S 2021 Expert Syst. Appl. 169 114331
[35] Tang X J and Zhang Z W 2008 2008 IEEE International Conference on Systems, Man and Cybernetics, October 12-15, 2008, Suntec, Singapore, p.~102
[36] Liu H, Wang B J, Lu J A and Li Z Y 2021 Acta Phys. Sin. 70 056401 (in Chinese)
[37] Yang F, Zhu J, Lun J Q, Zheng Z T, Tang Y and Wu J 2018 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design, May 9-11, 2018, Nanjing, China, p.~247
[38] Liu H, Shang Z C, Ren Z Y, Li Y, Zeng Z G and Lu J A 2022 Sci. China Technol. Sci. 65 1493
[39] Su X Y and Khoshgoftaar T M 2009 Adv. Artif. Intell. 2009 1
[40] Lü L Y, Medo M, Yeung C H, Zhang Y C, Zhang Z K and Zhou T 2012 Phys. Rep. 519 1
[41] Rigaux P 2004 Proceedings of the 2004 ACM Symposium on Applied Computing, March 14-17, 2004, Nicosia, Cyprus, p.~1682
[42] Li X L and Watanabe T 2013 Procedia Comput. Sci. 22 633
[43] Conry D, Koren Y and Ramakrishnan N 2009 Proceedings of the Third ACM Conference on Recommender Systems, October 23-25, 2009, New York, USA, p.~357
[44] Rose M E and Kitchin J R 2019 SoftwareX 10 100263
[45] Liu J G, Zhou Q, Guo Q, Yang Z H, Xie F and Han J T 2017 Sci. Rep. 7 10755
[46] Fu G Y, Chen F E, Liu J G and Han J T 2019 Physica A 525 409
[47] Zhou T, Ren J, Medo M and Zhang Y C 2007 Phys. Rev. E 76 046115
[48] Zhou T, Kuscsik Z, Liu J G, Medo M, Wakeling J R and Zhang Y C 2010 Proc. Natl. Acad. Sci. USA 107 4511
[49] Liu J G, Zhou T and Guo Q 2011 Phys. Rev. E 84 037101
[50] Guo Q, Leng R, Shi K and Liu J G 2012 Eur. Phys. J. B 85 1
[51] Guo Q, Song W J, Hou L, Zhang Y L and Liu J G 2014 Physica A 401 15
[52] Liu J G, Wang B H and Guo Q 2009 Int. J. Mod. Phys. C 20 285
[53] Pan X, Deng G S and Liu J G 2010 Phys. Procedia 3 1867
[54] Nie D C, An Y H, Dong Q, Fu Y and Zhou T 2015 Physica A 421 44
[55] Deng X F, Zhong Y S, Lü L Y, Xiong N X and Yeung C H 2017 Inf. Sci. 417 420
[56] Karimzadehgan M and Zhai C X 2012 Inf. Process. Manag. 48 725
[57] Röder M, Both A and Hinneburg A 2015 Proceedings of the 8th ACM International Conference on Web Search and Data Mining, January 31–Febuary 6, 2015, Shanghai, China, p. 399
[58] Sidorov G, Gelbukh A, Gómez-Adorno H and Pinto D 2014 Comput. Sist. 18 491
[59] Sarwar B, Karypis G, Konstan J and Riedl J 2001 Proceedings of the 10th International Conference on World Wide Web, May 1-5, 2001, Hong Kong, China, p. 285
[60] Binesh N and Rezghi M 2014 2014 6th Conference on Information and Knowledge Technology, May 28-30, 2014, Tehran, Iran, p. 146
[61] Aiolli F 2013 Proceedings of the 7th ACM Conference on Recommender Systems, October 12-16, 2013, Hong Kong, China, p. 273
[62] Chen C M, Wang T H, Wang C J and Tsai M F 2019 Proceedings of the 13th ACM Conference on Recommender Systems, September 16-20, 2019, Copenhagen, Denmark, p. 582
[63] Zhao Z Y, Sheng Y Q, Zhu M and Wang J L 2018 IEEE Access 6 67070
[64] Anjum O, Gong H Y, Bhat S, Hwu W M and Xiong J J 2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, November 3-7, 2019, Hong Kong, China, p. 518 |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|
