Abstract
This article presents a Multi-Agent approach for handling the problem of recommendation. The proposed system works via two main agents; namely, the matching agent and the recommendation agent. Experimental results showed that the proposed rough mereology based Multi-agent system for solving the recommendation problem is scalable and has possibilities for future modification and adaptability to other problem domains. Moreover, it succeeded in reducing the information overload while recommending relevant decisions to users. The system achieved high accuracy in ranking using users profile and information system profiles. The resulted value of the Mean Absolute Error (MAE) is acceptable compared to other recommender systems applied other computational intelligence approaches.
This work was partially supported by Grant of SGS No. SP2013/70, VSB - Technical University of Ostrava, Czech Republic., and was supported by the European Regional Development Fund in the IT4 Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070) and by the Bio-Inspired Methods: research, development and knowledge transfer project, reg. no. CZ.1.07/2.3.00/20.0073 funded by Operational Programme Education for Competitiveness, co-financed by ESF and state budget of the Czech Republic.
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Mahmood, M.A., El-Bendary, N., Platoš, J., Hassanien, A.E., Hefny, H.A. (2014). An Intelligent Multi-agent Recommender System. In: Abraham, A., Krömer, P., Snášel, V. (eds) Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 237. Springer, Cham. https://doi.org/10.1007/978-3-319-01781-5_19
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DOI: https://doi.org/10.1007/978-3-319-01781-5_19
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