Abstract
Aiming at that the mobile service recommendation results are inaccurate when the preparatory recommendation schemes are similar, this paper proposes a multi-situation Analytic Hierarchy Process based on Bayesian (MSAHPB). Firstly, the three-layer model of AHP was constructed. Then, introducing the multiple situation elements into the standard layer of the MSAHPB model. In order to determine the mutual influence between scenarios, different situations are used as criteria to estimate the impact of each situation on the recommended target. After that, establishing the relational judgment matrix for the adjacent two layers, in which the Bayesian is used instead of the method of assigning matrix by artificial experience. Deducing the weighted values of each situation by Bayesian formula, taking the prior probability of events as a benchmark. Then, to ensure that each matrix satisfies the consistency criterion, this paper tests the consistency of each judgment matrix according to the 1–9 scale, and calculates the matching degree between each scheme and target. Finally, using food service recommendation as an example, the experimental results showed that the method we proposed is effective.
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Acknowledgements
This work is supported in part by the Ministry of Education’s Funding for Returning Students Studying Abroad Projects of China (No. C2015003042), the Key Teaching Research project of Hebei University of Economics and Business (No. 2018JYZ06) and the Education technology research Foundation of the Ministry of Education (No. 2017A01020).
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Wang, W., Zhou, F., Cao, Y., Zhang, D., Sun, J. (2018). Multi-situation Analytic Hierarchy Process Based on Bayesian for Mobile Service Recommendation. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11063. Springer, Cham. https://doi.org/10.1007/978-3-030-00006-6_62
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DOI: https://doi.org/10.1007/978-3-030-00006-6_62
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