Abstract
The advancement of social communities and virtual interaction of thoughts have apparently made social networking one of the fastest-growing concepts. The interaction carries meanings beyond friendship and is applied to larger areas, such as communities and networks for business, trade, cinema, and broadcasting. In a social network, the user wants to find her/his interests, and by doing so the community, to which he/she belongs, develops and grows. However, the lack of important and useful information, and sometimes its inaccessibility, hinders users from establishing good connections, and as a consequence, it hinders expanding the community. The current paper presents a method of celebrity-based friend recommendation system based on the preferences and tendencies of the user and his/her friends. The proposed method introduces a novel way of extracting and modeling the recommendation process as a game theory problem with two main agents (Celebrity and Non-Celebrity) for selecting the members with more than 10000 followers, as celebrities, to be recommended. We have used the real data from Twitter social network celebrity members to test and analyze our proposed system from two aspects, i.e., recommender system and social network. The outcomes show that almost all the items recommended by MACeRS are celebrities (99%). Moreover, the accuracy of MACeRS is significantly better than other baseline methods.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13278-021-00845-w/MediaObjects/13278_2021_845_Fig1_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13278-021-00845-w/MediaObjects/13278_2021_845_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13278-021-00845-w/MediaObjects/13278_2021_845_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13278-021-00845-w/MediaObjects/13278_2021_845_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13278-021-00845-w/MediaObjects/13278_2021_845_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs13278-021-00845-w/MediaObjects/13278_2021_845_Fig6_HTML.png)
Similar content being viewed by others
References
Alvari H, Hashemi S, Hamzeh A (2011) Detecting overlapping communities in social networks by game theory and structural equivalence concept. Springer, Berlin, pp 620–630
Bian L, Holtzman H (2011) Online friend recommendation through personality matching and collaborative filtering. In: Proceedings of the UBICOMM, pp 230–235
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan):993–1022
Bliss CA, Frank MR, Danforth CM, Dodds PS (2014) An evolutionary algorithm approach to link prediction in dynamic social networks. J Comput Sci 5(5):750–764
Bu Z, Wang Y, Li H-J, Jiang J, Wu Z, Cao J (2019) Link prediction in temporal networks: integrating survival analysis and game theory. Inf Sci 498:41–61
Burke R (2007) Hybrid web recommender systems. The adaptive web. Springer, pp 377–408
Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapted Interact 12(4):331–370
Carvalho LAMC, Macedo HT (2013) Users’ satisfaction in recommendation systems for groups: an approach based on noncooperative games, pp 951–958
Chang C-J, Tsai T-L, Chen Y-H(2009) Utility and game-theory based network selection scheme in heterogeneous wireless networks. IEEE, pp 1–5
Cheng S, Zhang B, Zou G, Huang M, Zhang Z (2019) Friend recommendation in social networks based on multi-source information fusion. Int J Mach Learn Cybern 10(5):1003–1024
Chu C-H, Wu W-C, Wang C-C, Chen T-S, Chen J-J (2013). Friend recommendation for location-based mobile social networks. IEEE, pp 365–370
Debnath S, Ganguly N, Mitra P (2008) Feature weighting in content based recommendation system using social network analysis. pp 1041–1042
Del Olmo FH, Gaudioso E (2008) Evaluation of recommender systems: a new approach. Expert Syst Appl 35(3):790–804
Ding X, Jin X, Li Y, Li L (2013) Celebrity recommendation with collaborative social topic regression
Ge M, Delgado-Battenfeld C, Jannach D (2010) Beyond accuracy: evaluating recommender systems by coverage and serendipity. ACM, pp 257–260
Halkidi M, Koutsopoulos I (2011) A game theoretic framework for data privacy preservation in recommender systems. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, pp 629–644
Herlocker JL, Konstan JA, Riedl J (2000) Explaining collaborative filtering recommendations. pp 241–250
Jain P, Guru SK (2016) Friend recommendation using FLDA topic assignment model in microblogging system. Int J Innovat Res Comput Commun Eng 4(5)
Kwak H, Lee C, Park H, Moon S (2010) What is twitter, a social network or a news media? pp 591–600
Li J, Zhang L, Meng F, Li F (2014) Recommendation algorithm based on link prediction and domain knowledge in retail transactions. Procedia Comput Sci 31:875–881
Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031
Lim M, Abdullah A, Jhanjhi NZ, Supramaniam M (2019) Hidden link prediction in criminal networks using the deep reinforcement learning technique. Computers 8(1):8
Liu F, Li M (2019) A game theory-based network rumor spreading model: based on game experiments. Int J Mach Learn Cybern 10(6):1449–1457
Manshaei MH, Zhu Q, Alpcan T, Bacşar T, Hubaux J-P (2013) Game theory meets network security and privacy. ACM Comput Surv (CSUR) 45(3):1–39
Moradabadi B, Meybodi MR (2018) Link prediction in weighted social networks using learning automata. Eng Appl Artif Intell 70:16–24
Myerson RB (2013) Game theory. Harvard University Press, Cambridge
Papadimitriou A, Symeonidis P, Manolopoulos Y (2012) Fast and accurate link prediction in social networking systems. J Syst Softw 85(9):2119–2132
Parvathy, VS, and TK Ratheesh (2017) Friend recommendation system for online social networks: a survey, vol 2. IEEE, pp 359–365
Pazzani MJ, Billsus D (2007) Content-based recommendation systems. The adaptive web. Springer, pp 325–341
Popescul A, Ungar LH, Pennock DM, Lawrence S (2013) Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. arXiv preprint arXiv:1301.2303
Roozbahani Z, Rezaeenour J, Emamgholizadeh H, Bidgoly AJ (2020) A systematic survey on collaborator finding systems in scientific social networks. Knowl Inf Syst J 63(6)
Roughgarden T (2010) Algorithmic game theory. Commun ACM 53(7):78–86
Saga R, Okamoto K, Tsuji H, Matsumoto K (2013) Evaluating recommender system using multiagent-based simulator. In: Recent Progress in Data Engineering and Internet Technology. Springer, pp 155–162
Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. pp 285–295
Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. The adaptive web. Springer, pp 291–324
Schröder G , Thiele M, Lehner W (2011) Setting goals and choosing metrics for recommender system evaluations, vol 23. Chicago, USA, p page 53
Tajbakhsh MS, Aghababa MP, Solouk V, Akbari-Moghanjoughi A (2013) Friend recommendation based on the luscher color theory: Twitter use case. IEEE, pp 218–221
Tang F, Zhang B, Zheng J, Gu Y (2013) Friend recommendation based on the similarity of micro-blog user model. IEEE, pp 2200–2204
Tarkowski M, Michalak T, Wooldridge M (2019) A game-theoretic algorithm for link prediction. arXiv preprint arXiv:1912.12846
Trestian R, Ormond O, Muntean G-M (2011) Reputation-based network selection mechanism using game theory. Phys Commun 4(3):156–171
Trestian R, Ormond O, Muntean G-M (2012) Game theory-based network selection: Solutions and challenges. IEEE Commun Surv Tutor 14(4):1212–1231
Tsvetovat M, Kouznetsov A (2011) Social Network Analysis for Startups: Finding connections on the social web. O’Reilly Media Inc
Tuan TM, Chuan PM, Ali M, Ngan TT, Mittal M et al (2019) Fuzzy and neutrosophic modeling for link prediction in social networks. Evolv Syst 10(4):629–634
Verma J, Gupta S, Mukherjee D, Chakraborty T (2019) Heterogeneous edge embedding for friend recommendation. Springer, Berlin, pp 172–179
Xu Y, Zhou D, Ma J (2019) Scholar-friend recommendation in online academic communities: an approach based on heterogeneous network. Decis Support Syst 119:1–13
Zaier Z, Godin R, Faucher L(2008) Evaluating recommender systems. IEEE, pp 211–217
Zhang M, Yixin C (2018) Link prediction based on graph neural networks. In: Advances in Neural Information Processing Systems, pp 5165–5175
Zhao T, Zhao H, King I (2015) Exploiting game theoretic analysis for link recommendation in social networks, pp 851–860
Zheng N, Song S, Bao H (2015) A temporal-topic model for friend recommendations in Chinese microblogging systems. IEEE Trans Syst Man Cybern Syst 45(9):1245–1253
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Tajbakhsh, M.S., Emamgholizadeh, H., Solouk, V. et al. Multi-agent celebrity recommender system (MACeRS): Twitter use case. Soc. Netw. Anal. Min. 12, 11 (2022). https://doi.org/10.1007/s13278-021-00845-w
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13278-021-00845-w