An Intelligent Multi-Agent Recommender System using Rough Mereology

Citation:
Mahmood, M. A., N. El-Bendary, A. E. Hassanien, and H. A. Hefny, "An Intelligent Multi-Agent Recommender System using Rough Mereology", In Proceedings of the 4th International Conference on Innovations in Bio-inspired Computing and Applications. Advances in Intelligent Systems and Computing (Springer) Volume 237, pp 201-213, 2013.

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.

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