Abstract
“One’s network is their net worth” is the common saying most successful people follow. Building a good network can help in both personal growth and that of one’s business. However, a network must be reliable and involves people with a positive attitude, a likeable nature, and an affinity to attract other people through their power of influence on social media. These are a few characteristics that help in establishing a network that is trustworthy and in targeting the right audience who can help expand an e-commerce business through their strong impact of reviews, posts, likes, and friend circle. Through this paper, we aim to target people who are more socially likeable and open to building new social connections on social media platforms (Facebook is our platform of focus), which can be used for building individual connections or advertising and promoting products through effective networking.
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References
Bharati M, Ramageri B (2010) Data Mining Techniques And Applications. Indian J Comput Sci Eng 1
Singh M, Singh G (2018) Impact of social media on e-commerce. Int J Eng Technol UAE 7: 21–26. https://doi.org/10.14419/ijet.v7i2.30.13457
Crandall D, Cosley D, Huttenlocher D, Kleinberg J, Suri S (2008) Feedback effects between similarity and social influence in online communities. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 160–168. https://doi.org/10.1145/1401890.1401914
Falch M, Henten A, Tadayoni R, Windekilde I (2009) Business models in social networking
Cen Y, Zhang J, Wang G, Qian Y, Meng C, Dai Z, Yang H, Tang J (2019) Trust relationship prediction in Alibaba e-commerce platform. IEEE Trans Knowl Data Eng: 1–1. https://doi.org/10.1109/tkde.2019.2893939
Mele I (2012) Early-adopter graph and its applications to web-page recommendation. In: CIKM’12: Proceedings of the 21st ACM international conference on information and knowledge management. Sapienza, University of Rome, Italy, Aristides Gionis and Francesco Bonchi, Yahoo!ResearcherBarcelona, Spain), pp 1682–1686, Oct 2012
Backstrom L, Leskovec J (2011) Supervised random walks: predicting and recommending links in social networks. In WSDM’11, pp 635–644
A matrix factorization technique with trust propagation for recommendation in social networks-RecSys’10. In: Proceedings of the fourth ACM conference on recommender systems, pp 135–142, Sept 2010
Chen Y-F (2008) Herd behavior in purchasing books online. Comput Human Behav
Liu S, Zhang L, Yan Z (2018) Predict pairwise trust based on machine learning in online social networks: a survey. IEEE Access 1–1. https://doi.org/10.1109/access.2018.2869699
Amit G, Lakshmanan LVS Learning influence probabilities in social networks. University of British Columbia, Vancoucer, BC, Canada, and Francesco Bonchi, Yahoo!ResearcherBarcelona, Spain
Singh J, Irani S, Rana N, Dwivedi Y, Saumya S, Roy P (2016). Predicting the “helpfulness” of online consumer reviews. J Bus Res 70. https://doi.org/10.1016/j.jbusres.2016.08.008
Fourt LA, Joseph WW Early prediction of market success for new grocery products. American Marketing Association
Jiang L, Cheng Y, Yang L, Li J, Yan H, Wang X (2017) A trust based collaborative filtering algorithm for e-commerce recommendation system. J Ambient Intell Human Comput
Nikhita V, Srinivasan P Predicting trust relations among users in a social network: the role of influence, cohesion and valence. Department of Computer Science and Engineering Ohio State University, vedula@cse.ohio-state.edu, srini@cse.ohio-state.edu. Valerie L. Shalin Department of Psychology and Kno.e.sis Wright State University
Wang B, Xiong S, Huang Y, Li X (2018) Review rating prediction based on user context and product context. Appl Sci 8:1849. https://doi.org/10.3390/app8101849
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Lavanya, R., Saksena, A., Singh, A. (2021). Effective Networking on Social Media Platforms for Building Connections and Expanding E-commerce Business by Analyzing Social Networks and User’s Nature and Reliability. In: Hemanth, D., Vadivu, G., Sangeetha, M., Balas, V. (eds) Artificial Intelligence Techniques for Advanced Computing Applications. Lecture Notes in Networks and Systems, vol 130. Springer, Singapore. https://doi.org/10.1007/978-981-15-5329-5_47
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