Abstract
With the rapidly increasing popularity of social media applications, decentralized control and ownership is taking more attention to preserve user’s privacy. However, the lack of central control in the decentralized social network poses new issues of collaborative decision making and trust to this permission-less environment. To tackle these problems and fulfill the requirements of social media services, there is a need for intelligent mechanisms integrated to the decentralized social media that consider trust in various aspects according to the requirement of services. In this paper, we describe an adaptive microservice-based design capable of finding relevant communities and accurate decision making by extracting semantic information and applying role-stage model while preserving anonymity. We apply this information along with exploiting Pareto solutions to estimate the trust in accordance with the quality of service and various conflicting parameters, such as accuracy, timeliness, and latency.
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Acknowledgments
This work was accomplished as a part of project “ARTICONF" (http://www.articonf.eu/), funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No 644179. The authors would also like to thank anonymous reviewers for their valuable comments.
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Najafabadi Samani, Z., Lercher, A., Saurabh, N., Prodan, R. (2020). A Semantic Model with Self-adaptive and Autonomous Relevant Technology for Social Media Applications. In: Schwardmann, U., et al. Euro-Par 2019: Parallel Processing Workshops. Euro-Par 2019. Lecture Notes in Computer Science(), vol 11997. Springer, Cham. https://doi.org/10.1007/978-3-030-48340-1_34
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DOI: https://doi.org/10.1007/978-3-030-48340-1_34
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