Abstract
The user-centered service paradigm has attracted extensive attention from academia and industry. It advocates taking customer requirements as the orientation, maximizing customer satisfaction as the objective, then targeting to carry out market segmentation and multi-level SLA customization. There are two main challenges: personalized preferences of large-scale customers and resource constraints. In this paper, we propose a resource-constrained multi-level SLA customization approach based on QoE analysis of large-scale customers. With a deep generative network, we fit satisfaction mapping functions and infer the customer’s personalized preference interval for each QoS. Then, based on the theory of granular computing, a multi-level, multi-perspective and multi-scale granular structure for service customization is constructed. Finally, the best match between users with personalized preferences and resources with differentiated qualities is mined to obtain a reduced and balanced multi-level SLA customization scheme. This paper conducts experiments based on the real data of a hotel booking platform and proves that the method performs well in service customization granularity, preference coverage and matching accuracy. The method is an on-demand optimization and avoids over-optimization. The final customized solutions can not only meet the personalized preferences but also give play to the advantages of different quality resources.
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Acknowledgments
Research in this paper is partially supported by the National Key Research and Development Program of China (No 2022YFF0903100) and the National Natural Science Foundation of China (61832014, 61832004).
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Li, M., Xu, H., Xu, X., Wang, Z. (2023). A Resource-Constrained Multi-level SLA Customization Approach Based on QoE Analysis of Large-Scale Customers. In: Indulska, M., Reinhartz-Berger, I., Cetina, C., Pastor, O. (eds) Advanced Information Systems Engineering. CAiSE 2023. Lecture Notes in Computer Science, vol 13901. Springer, Cham. https://doi.org/10.1007/978-3-031-34560-9_35
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