Abstract
Recent advances in the computing infrastructure have led to the realization of advanced solutions for catering to the needs of human users. Obtaining recommendations from the network of things that are inspired by the social behavior of humans is becoming an important research topic. The main crux of such systems is to provide the users with recommendations of products and services by considering the opinions of the user’s social circle. The social circle of the user is inferred from the various online social networking sites that the user is part of. However, while considering these systems, there is a need to evaluate not only the opinion of the user’s social circle about the product/service provider but also the past experiences of the user with the providers as well as well as the strength of the relationships of the user with other users in his social circle. The work proposed in this paper aims to provide trusted recommendations to the user by incorporating multiple aspects of the social behavior of the user. Theoretical evaluation of the proposed work clearly indicates the enhanced efficiency and trustworthiness of socially inspired recommendation systems.
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Sinha, A., Kumar, P., Singh, M. (2022). Trusted Recommendation Model for Social Network of Things. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1387. Springer, Singapore. https://doi.org/10.1007/978-981-16-2594-7_67
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