Abstract
A fan page is a kind of a social network. Social network marketing (SNM) is a form of Internet marketing involving the creation and sharing of content on social media networks to achieve marketing and selling goals. In addition, precise SNM requires sufficient data and analysis in terms of making accurate online recommendations. This study examines the experience of various Taiwanese fan page users utilizing a market survey, a total of 1032 valid questionnaire data, and the questionnaire is divided into five sections with 33 items in terms of a big data structure based on a relational database on the first research stage. All questions use nominal and ordinal scales. In the second stage, this study develops a personalized recommendation system (PRS) using big data analytics approach, including cluster analysis and association rules. This study shows how the research results can obtain fans behavior knowledge by examining different group profiles and develop rule-based recommendation approach to generate personalized recommendations for building a SNM mechanism.
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References
Agrawal R, Shafer J (1996) Parallel Mining of Association Rules. IEEE Trans Knowl Data Eng 8(6):962–969. https://doi.org/10.1109/69.553164
Agrawal R, Imilienski T, Swami A (1993) Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International conference on management of data, pp 207–216
Al-Jarrah OY, Yoo D, Muhaidat S, Karagiannidis K, Taha K (2015) Efficient machine learning for big data: a review. Big Data Res 2(3):87–93. https://doi.org/10.1016/j.bdr.2015.04.001
Ballestar MT, Grau-Carles P, Sainz J (2018) Customer segmentation in e-commerce: applications to the cashback business model. J Business Res 88(7):407–414. https://doi.org/10.1016/j.jbusres.2017.11.047
Chang WL, Diaz N, Hung CK (2015) Estimating trust value: a social network perspective. Information Syst Front 17(6):1381–1400. https://doi.org/10.1007/s10796-014-9519-0
Chen P (1976) The entity-relationship model-toward a unified view of data. ACM Trans Database Syst 1(1):9–36. https://doi.org/10.1145/320434.320440
Chih WH, Hsu LC, Liou DK (2017) Understanding virtual community members’ relationships from individual, group, and social influence perspectives. Industrial Manag Data Syst 117(6):990–1010. https://doi.org/10.1108/IMDS-03-2016-0119
Choi S (2020) When digital trace data meet traditional communication theory. Soc Sci Comput Rev 38(1):91–107. https://doi.org/10.1177/0894439318788618
Codd EF (1970) A relational model of data for large shared data banks. Commun ACM. 13(6):377–438. https://doi.org/10.1145/362384.362685
Corbellini A, Mateos C, Godoy D (2015) An architecture and platform for developing distributed recommendation algorithms on large-scale social networks. J Information Sci 41(5):686–704. https://doi.org/10.1177/0165551515588669
de Souza JV, Gomes J, de Souza Filho FM, de Oliveira Julio AM, de Souza JF (2020) A systematic mapping on automatic classification of fake news in social media. Soc Netw Anal Min. https://doi.org/10.1007/s13278-020-00659-2
Doostmohammadian M, Rabiee HR, Khan UA (2020) Centrality-based epidemic control in complex social networks. Soc Netw Anal Min. https://doi.org/10.1007/s13278-020-00638-7
Fonseca A, Cabral B (2017) Prototyping a GPGPU neural network for deep-learning big data analysis. Big Data Res 8(3):50–56. https://doi.org/10.1016/j.bdr.2017.01.005
Gao M, Ling B, Yang L, Wen J, Xiong Q, Li S (2019) From similarity perspective: a robust collaborative filtering approach for service recommendations. Front Comput Sci 13(2):231–246. https://doi.org/10.1007/s11704-017-6566-y
Goh KY, Heng CS, Lin Z (2013) Social media brand community and consumer behavior: quantifying the relative impact of user- and marketer-generated content. Information Syst Res 24(1):88–107. https://doi.org/10.2139/ssrn.2048614
Gu X, Yang H, Tang J, Zhang J (2018) Profiling Web users using big data. Soc Netw Anal Min. https://doi.org/10.1007/s13278-018-0495-0
Hajarian M, Bastanfard A, Mohammadzadeh J, Khalilian M (2019) A personalized gamification method for increasing user engagement in social networks. Soc Netw Anal Min. https://doi.org/10.1007/s13278-019-0589-3
He J, Shao B (2018) Examining the dynamic effects of social network advertising: a semiotic perspective. Telematics Informatics 35(2):504–516. https://doi.org/10.1016/j.tele.2018.01.014
Ho KW, See-To WK (2018) The impact of the uses and gratifications of tourist attraction fan page. Internet Res 28(3):587–603. https://doi.org/10.1108/IntR-04-2017-0175
Huang SL, Chen CT (2018) How consumers become loyal fans on facebook. Comput Hum Behav 82(5):124–135. https://doi.org/10.1016/j.chb.2018.01.006
Huang CT, Huang TJ (2016) The evolution of fan kingdom: the rising, expansion, and challenges of human brands. Asia Pacific J Mark Logist 28(4):683–708. https://doi.org/10.1108/APJML-07-2015-0111
Ilhan BE, Kübler RV, Pauwels KH (2018) Battle of the brand fans: impact of brand attack and defense on social media. J Interact Mark 43(3):33–51. https://doi.org/10.1016/j.intmar.2018.01.003
Jukić N, Sharma A, Nestorov S, Jukić B (2015) Augmenting data warehouses with big data. Information Syst Manag 32(3):200–209. https://doi.org/10.1080/10580530.2015.1044338
Khan I, Dongping H (2017) Variations in the diffusion of social media content across different cultures: a communicative ecology perspective. J Global Information Technol Manag 20(3):156–170. https://doi.org/10.1080/1097198X.2017.1354598
Khobzi H, Teimourpour B (2014) How significant are users’ opinions in social media? Int J Account Information Manag 22(4):254–272. https://doi.org/10.1108/IJAIM-02-2014-0010
Kouris IN, Makris CH, Tsakalidis AK (2005) Using information retrieval techniques for supporting data mining. Data Knowl Eng 52(3):353–383. https://doi.org/10.1016/j.datak.2004.07.004
Laney D (2001) 3D Data management: controlling data volume, veocity, and variety. META Group article”, available at: https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf (accessed 6 Feb 2001)
Lee DH, Brusilovsky P (2017) Improving personalized recommendations using community membership information. Inf Process Manag 53(5):1201–1214. https://doi.org/10.1016/j.ipm.2017.05.005
Liao HS, Chang HK (2016) A rough set-based association rule approach for a recommendation system for online consumers. Inf Process Manag 52(4):1142–1160. https://doi.org/10.1016/j.ipm.2016.05.003
Liao HS, Chu PH, Hsiao PY (2012) Data mining techniques and applications–a decade review from 2000 to 2011. Expert Syst Appl 39:11303–11311. https://doi.org/10.1016/j.eswa.2012.02.063
Lin SW, Liu YC (2012) The effects of motivations, trust, and privacy concern in social networking. Serv Bus 6(4):411–424. https://doi.org/10.1007/s11628-012-0158-6
Ma T, McGroarty F (2017) Social machines: how recent technological advances have aided financialisation. J Information Technol 32(3):234–250. https://doi.org/10.1057/s41265-017-0037-7
Malekhosseini R, Hosseinzadeh M, Navi K (2018) An investigation into the requirements of privacy in social networks and factors contributing to users’ concerns about violation of their privacy. Soc Netw Anal Min. https://doi.org/10.1007/s13278-018-0518-x
Manthiou A, Tang LR, Bosselman R (2014) Reason and reaction: the dual route of the decision-making process on facebook fan pages. Electron Mark 24(4):297–308
Modarresi K (2016) Recommendation system based on complete personalization. Procedia Comput Sci 80:2190–2204. https://doi.org/10.1016/j.procs.2016.05.379
Nassar H, Benson AR, Gleich DF (2020) Neighborhood and pagerank methods for pairwise link prediction. Soc Netw Anal Min. https://doi.org/10.1007/s13278-020-00671-6
Rajabzadeh S, Shahsafi P, Khoramnejadi M (2020) A graph modification approach for k-anonymity in social networks using the genetic algorithm. Soc Netw Anal Min. https://doi.org/10.1007/s13278-020-00655-6
Rathore M, Ahmad A, Paul A (2018) Exploiting encrypted and tunneled multimedia calls in high-speed big data environment. Multimedia Tools Appl 77(4):4959–4984. https://doi.org/10.1007/s11042-017-4393-7
Risselada H, Verhoef C, Bijmolt HA (2016) Indicators of opinion leadership in customer networks: self-reports and degree centrality. Mark Lett 27(3):449–460. https://doi.org/10.1007/s11002-015-9369-7
Rodriguez A, Okamura K (2020) Enhancing data quality in real-time threat intelligence systems using machine learning. Soc Netw Anal Min. https://doi.org/10.1007/s13278-020-00707-x
Santos FF, Domingues MA, Sundermann CV, Carvalho VO, Rezende SO (2018) Latent association rule cluster based model to extract topics for classification and recommendation applications. Expert Syst Appl 112(10):34–60. https://doi.org/10.1016/j.eswa.2018.06.021
Schultz CD (2017) Proposing to your fans: Which brand post characteristics drive consumer engagement activities on social media brand pages? Electron Commer Res Appl 26(10):23–34. https://doi.org/10.1016/j.elerap.2017.09.005
Sebei H, Ali Hadj Taieb M, Aouicha MB (2018) Review of social media analytics process and big data pipeline. Soc Netw Anal Min. https://doi.org/10.1007/s13278-018-0507-0
Shang L, Zhang Y, Zhang D, Wang D (2020) FauxWard: a graph neural network approach to fauxtography detection using social media comments. Soc Netw Anal Min. https://doi.org/10.1007/s13278-020-00689-w
Sheu JJ, Chu KT (2017) Mining association rules between positive word-of-mouth on social network sites and consumer acceptance: a study for derivative product of animations, comics, and games. Telematics Inform 34(4):22–33. https://doi.org/10.1016/j.tele.2016.12.010
Shi J, Hu P, Lai KK, Chen G (2018) Determinants of users’ information dissemination behavior on social networking sites: an elaboration likelihood model perspective. Internet Res 28(2):393–418. https://doi.org/10.1108/IntR-01-2017-0038
Supattana S, Papasratorn B (2018) An architectural framework for developing a recommendation system to enhance vendors’ capability in C2C social commerce. Soc Netw Anal Min. https://doi.org/10.1007/s13278-018-0500-7
Sutanto T, Nayak R (2018) Fine-grained document clustering via ranking and its application to social media analytics. Soc Netw Anal Min. https://doi.org/10.1007/s13278-018-0508-z
Tian X, Liu L (2017) Does big data mean big knowledge? integration of big data analysis and conceptual model for social commerce research. Electron Commer Res 17(1):169–183. https://doi.org/10.1007/s10660-016-9242-7
Triantafillidou A, Siomkos G (2018) The impact of facebook experience on consumers’ behavioral brand engagement. J Res Interact Mark 12(2):164–192. https://doi.org/10.1108/JRIM-03-2017-0016
Trieu VH (2017) Getting value from business Intelligence systems: a review and research agenda. Decis Support Syst 93(2):111–124. https://doi.org/10.1016/j.dss.2016.09.019
Ture M, Kurt I, Turhan KA, Ozdamar K (2005) Comparing classification techniques for predicting essential hypertension. Expert Syst Appl 16(2):379–384. https://doi.org/10.1016/j.eswa.2005.04.014
Wang S, Wang H (2016) Renewal of Classics: Database Technology for all Business Majors. J Comput Information Syst 56(3):211–217. https://doi.org/10.1080/08874417.2016.1153898
Wang YF, Chuang YL, Hsu MH, Keh HC (2004) A personalized recommender system for the cosmetic business. Expert Syst Appl 26(1):42–52. https://doi.org/10.1016/j.eswa.2003.10.001
Xiao J, Wang M, Jiang B (2018) A personalized recommendation system with combinational algorithm for online learning. J Ambient Intell Humaniz Comput 9(3):667–677. https://doi.org/10.1007/s12652-017-0466-8
Xu W, Sun J, Ma J, Du W (2016) A personalized information recommendation system for R&D project opportunity finding in big data contexts. J Netw Comput Appl 59(1):362–369. https://doi.org/10.1016/j.jnca.2015.01.003
Yang S, Korayem M, AlJadda K, Grainger T, Natarajan S (2017) Combining content-based and collaborative filtering for job recommendation system: a cost-sensitive statistical relational learning approach. Knowl-Based Syst 136(11):37–45. https://doi.org/10.1016/j.knosys.2017.08.017
Yu L (2018) A novel E-commerce model and system based on O2O sports community. IseB. https://doi.org/10.1007/s10257-018-0385-z
Yu Y, Gao Y, Wang H, Wang R (2018) Joint user knowledge and matrix factorization for recommender systems. World W Web 21(4):1141–1163. https://doi.org/10.1007/s11280-017-0476-7
Zhang Z, Sun R, Zhao C, Wang J, Chang CK, Gupta BB (2017) CyVOD: a novel trinity multimedia social network scheme. Multimedia Tools Appl 76(18):18513–18529. https://doi.org/10.1007/s11042-016-4162-z
Zhang L, Luo M, Boncella J (2020) Product information diffusion in a social network. Electron Commer Res 20(1):3–19. https://doi.org/10.1007/s10660-018-9316-9
Zheng X, Luo Y, Sun L, Ding X, Zhang J (2018) A novel social network hybrid recommender system based on hypergraph topologic structure. World W Web 21(4):985–1013. https://doi.org/10.1007/s11280-017-0494-5
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Liao, SH., Yang, CA. Big data analytics of social network marketing and personalized recommendations. Soc. Netw. Anal. Min. 11, 21 (2021). https://doi.org/10.1007/s13278-021-00729-z
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DOI: https://doi.org/10.1007/s13278-021-00729-z