Cluster Analysis Research on Consumers’ Perceived Recommendation Trust
AUTHORS
Olasupo Sule,University of Pretoria, Pretoria, South Africa
Bheki Surian,University of Pretoria, Pretoria, South Africa
ABSTRACT
Emerging social commerce relies on social media and is a new growth point for the development of e-commerce. However, it is difficult to cluster the recommendation information to reflect the subjectivity of consumers and the relationship between subjects. This paper constructs a clustering method based on consumer perception recommendation trust in the context of large-scale social networks and improves on subjective logic methods. Integrate trust characteristics into subjective logic trust transfer algorithm. Transform objective recommendation information into consumer subjective and differentiated perceived trust. Extract the similarity of perceptual recommendation trust and relationship intimacy from social networks to generate a normal matrix, and divide it by the method of spectral halving. A consumer perception trust network is extracted from social networks, a clustering method for consumer perception trust in social business is proposed from the perspective of complex network division, and a clustering center identification and update mechanism is designed for the high dynamic characteristics of social networks. The experimental results prove that: social business merchants and platforms quickly identify consumer perception of trust orientation, and provide methodological support for merchants to formulate trust-based precision marketing strategies for social business.
KEYWORDS
Social business, Perceived trust, Recommendation information, Clustering method
REFERENCES
[1] T. S. Andrew and T. Olivier, “Deriving value from social commerce networks,” Social Science Electronic Publishing, vol.47, no. 2, pp.215-228, (2010)
[2] C. Renata and P. Zhang, “Social commerce: Looking back and forward,” Proceedings of the American Society for Information Science and Technology, vol.48, no.1, pp.1-10, (2012)
[3] M. Hassan, Z. Iqbal, and B. Khanum, “The role of trust and social presence in social commerce purchase intention,” Pakistan Journal of Commerce and Social Sciences (PJCSS), vol.12, no.1, pp.111-135, (2018)
[4] R. B. Lount Jr., “The impact of positive mood on trust in interpersonal and intergroup interactions,” Journal of Personality and Social Psychology, vol.98, no.3, pp.420, (2010)
[5] Y. Su-Rong, Z. Xiao-Lin, Y. W. William, W. Song, and W. Y. Zhang,” A graph-based comprehensive reputation model: Exploiting the social context of opinions to enhance trust in social commerce,” Information Sciences, vol.318, pp.51-72, (2015)
[6] J. B. Johnson and H. P. Reingen, “Social ties and word-of-mouth referral behavior,” Journal of Consumer Research, vol.14, no.3, pp.350-362, (1987)
[7] Y. Jin and P. H. Xiang, “A trust transitivity model of farmers for purchasing agricultural means of production online,” Icic Express Letters, vol.8, no.3, pp.927-933, (2014)
[8] Z. Lina, Z. Ping, and Z. Hans-Dieter, “Social commerce research: An integrated view,” Electronic Commerce Research and Applications, vol.12, no.2, pp.61-68, (2013)
[9] K. Sanghyun and P. Hyunsun, “Effects of various characteristics of social commerce (s-commerce) on consumers’ trust and trust performance,” International Journal of Information Management, vol.33, no.2, pp.318-332, (2013)
[10] H. Nick and H. Mahmood, “A research framework for social commerce adoption,” Information Management and Computer Security, vol.21, no.3, pp.144-154, (2013)
[11] J. Weisberg, D. Te’ Eni, and L. Arman, “Past purchase and intention to purchase in e-commerce: The mediation of social presence and trust,” Internet Research Electronic Networking Applications and Policy, vol.21, no.1, pp.82-96, (2011)
[12] S. Dong-Hee, “User experience in social commerce: In friends we trust,” Behaviour and Information Technology, vol.32, no.1, pp.52-67, (2013)
[13] J. Berger, “Word of mouth and interpersonal communication: A review and directions for future research,” Journal of Consumer Psychology, vol.24, no.4, pp.586-607, (2014)
[14] Y. Su-Rong, Z. Xiao-Lin, W. Yan, S. William Wei, and Z. Wen Yu, “A graph-based comprehensive reputation model: Exploiting the social context of opinions to enhance trust in social commerce,” Information Sciences, vol.318, pp.51-72, (2015)
[15] M. Granovetter and R. Soong, “Threshold models of interpersonal effects in consumer demand,” Journal of Economic Behavior and Organization, vol.7, no.1, pp.83-99
[16] A. Capocci, V. D. P. Servedio, G. Caldarelli, and F. Colaiori, “Detecting communities in large networks,” Physica A Statistical Mechanics and its Applications, vol.352, pp.2-4, pp.669-676, (2005)
[17] D. H. Mcknight, C. J. Kacmar, and V. Choudhury, “Dispositional trust and distrust distinctions in predicting high-and low-risk Internet expert advice site perceptions,” e-Service Journal, vol.3, no.2, pp.35-58, (2004)
[18] W. Guohua, H. Xiaorui. W. Yuhong, “Effects of perceived interactivity，perceived web assurance and disposition to trust on initial online trust,” Journal of Computer-mediated Communication, vol.16, no.1, pp.1-26, (2010)
[19] R. Saadi, O. Hasan, J. M. Pierson, and L. Brunie, “Establishing trust beliefs based on a uniform disposition to trust,” International IEEE Conference on Signal-Image Technologies and Internet-Based System, (2017)
[20] W. W. Stephen, N. Waros, and H. Chia-Hung, “Trust disposition，trust antecedents, trust, and behavioral intention,” Service Industries Journal, vol.35, no.10, pp.555-572, (2015)
[21] A. J. Jeang, “The consensus operator for combining beliefs,” Artificial Intelligence, vol.141, no.1, pp.157-170, (2002)
[22] A. J. Jesang, E. Gray, and M. Kinateder, “Simplification and analysis of transitive trust networks,” Web Intelligence and Agent Systems: An International Journal, vol.4, no.2, pp.139-161, (2006)