Abstract
With the vigorous development of the platform-based service ecosystem represented by e-commerce, service recommendation is used as a personalized matching method. There exist some service recommendation strategies that mainly focus on high popularity services and ignore non-popular ones. This will not only lead to oligopoly, but also be detrimental to the health of the platform-based service ecosystem. In addition, the health evaluation indicators for this kind of ecosystems are mostly qualitative and single. In view of the above phenomenon and based on the system view of balance and health, a health index model (HIM) is proposed to measure the health from two aspects quantitatively: stability and sustainability. Specifically, the model includes the system activity and organizational structure reflecting stability, as well as the productivity and vitality reflecting sustainability, which helps to illustrate the health status of the platform-based service ecosystem from the perspective of multi-dimensional integration. Additionally, this paper analyzes the factors affecting the health of this ecosystem based on HIM. In this work, a platform-based service ecosystem simulation model is constructed by using the computational experiment method to verify the effectiveness of HIM. The simulation results show that the HIM can reasonably measure the health of such ecosystems, which has guiding significance for the overall management and sound development of e-commerce platforms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ambulgekar, H.P., Pathak, M.K., Kokare, M.B.: A survey on collaborative filtering: tasks, approaches and applications. In: Chakraborty, M., Chakrabarti, S., Balas, V.E., Mandal, J.K. (eds.) Proceedings of International Ethical Hacking Conference 2018. AISC, vol. 811, pp. 289–300. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1544-2_24
Bai, B., Fan, Y., Tan, W., Zhang, J.: DLTSR: a deep learning framework for recommendation of long-tail web services. IEEE Trans. Serv. Comput. 13, 73–85 (2017)
Cardoso, J., Sheth, A.P., Miller, J.A., Arnold, J., Kochut, K.J.: Quality of service for workflows and web service processes. J. Web Semant. 1(3), 281–308 (2004)
Chen, L., Huang, Q.: An exact calculation method for Gini coefficient and its application in China. J. Discrete Math. Sci. Crypt. 21(6), 1235–1240 (2018)
Chen, X., Zheng, Z., Liu, X., Huang, Z., Sun, H.: Personalized QoS-aware web service recommendation and visualization. IEEE Trans. Serv. Comput. 6(1), 35–47 (2011)
Chen, X., Zheng, Z., Lyu, M.R.: QoS-aware web service recommendation via collaborative filtering. In: Bouguettaya, A., Sheng, Q., Daniel, F. (eds.) Web Services Foundations, pp. 563–588. Springer, Heidelberg (2014). https://doi.org/10.1007/978-1-4614-7518-7_22
Costanza, R., et al.: The value of ecosystem services: putting the issues in perspective. Ecol. Econ. 25(1), 67–72 (1998)
Fu, M., Qu, H., Moges, D., Lu, L.: Attention based collaborative filtering. Neurocomputing 311, 88–98 (2018)
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, Cambridge (2010)
Kiinzli, S., Poletti, F., Benini, L., Thiele, L.: Combining simulation and formal methods for system-level performance analysis. In: Proceedings of the Design Automation and Test in Europe Conference, vol. 1, pp. 236–241 (2006)
Li, S., Fan, Y.: Research on the service-oriented business ecosystem. In: 2011 3rd International Conference on Advanced Computer Control, pp. 502–505. IEEE (2011)
Napitupulu, D., et al.: Analysis of student satisfaction toward quality of service facility. Phys. Educ. 954(1), 012019 (2018). http://arxiv.org/abs/Physics
Rapport, D.J., Costanza, R., McMichael, A.: Assessing ecosystem health. Trends Ecol. Evol. 13(10), 397–402 (1998)
Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)
Schaeffer, D.J., Herricks, E.E., Kerster, H.W.: Ecosystem health: I. measuring ecosystem health. Environ. Manag. 12(4), 445–455 (1988)
Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in e-commerce (2014)
Singh, M., Matsui, Y.: Effect of long tail and trust on customer motivation behind online shopping use: comparative study between physical product and service product. In: PACIS, p. 221 (2017)
Spellerberg, I.F., Fedor, P.: A tribute to claude shannon (1916–2001) and a plea for more rigorous use of species richness, species diversity and the ‘shannon-wiener’ index. Glob. Ecol. Biogeogr. 12(3), 177–179 (2003)
Spohrer, J., Maglio, P.P., Bailey, J., Gruhl, D.: Steps toward a science of service systems. Computer 40(1), 71–77 (2007)
Wen, D., Yuan, Y., Li, X.R.: Artificial societies, computational experiments, and parallel systems: an investigation on a computational theory for complex socioeconomic systems. IEEE Trans. Serv. Comput. 6(2), 177–185 (2012)
Xiao, J., Chen, S., He, Q., Feng, Z., Xue, X.: An android application risk evaluation framework based on minimum permission set identification. J. Syst. Softw. 163, 110533 (2020)
Xie, F., Chen, Z., H., Feng, X., Hou, Q.: TST: Threshold based similarity transitivity method in collaborative filtering with cloud computing. Tsinghua Science and Technology (2013)
Xue, F., He, X., Wang, X., Xu, J., Liu, K., Hong, R.: Deep item-based collaborative filtering for top-n recommendation. ACM Trans. Inf. Syst. (TOIS) 37(3), 1–25 (2019)
Xue, X., Kou, Y.M., Wang, S.F., Liu, Z.Z.: Computational experiment research on the equalization-oriented service strategy in collaborative manufacturing. IEEE Trans. Serv. Comput. 11(2), 369–383 (2016)
Acknowledgment
This work is supported by the National Key R&D Program of China grant No.2017YFB1401201, the National Natural Science Key Foundation of China grant No.61832014 and No.61972276the Shenzhen Science and Technology Foundation grant JCYJ20170816093943197, and the Natural Science Foundation of Tianjin City grant No.19JCQNJC00200.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Feng, Y., Feng, Z., Xue, X., Chen, S. (2021). HIM: A Systematic Model to Evaluate the Health of Platform-Based Service Ecosystems. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 349. Springer, Cham. https://doi.org/10.1007/978-3-030-67537-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-67537-0_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67536-3
Online ISBN: 978-3-030-67537-0
eBook Packages: Computer ScienceComputer Science (R0)