An Intelligent Multi-agent Recommender System

  • Mahmood A. Mahmood
  • Nashwa El-Bendary
  • Jan Platoš
  • Aboul Ella Hassanien
  • Hesham A. Hefny
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 237)

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.

Keywords

rough mereology multi-agent recommender system 

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References

  1. 1.
    Gauch, S., Speretta, M., Chandramouli, A., Micarelli, A.: User Profiles For Personalized Information Access. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 54–89. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Macho, S., Torrens, M., Faltings, B.: A Multi-Agent Recommender System For Planning Meetings. In: Proc. of the 4th International Conference on Autonomous Agents, Workshop on Agent-based Recommender Systems, WARS 2000 (2000)Google Scholar
  3. 3.
    Morais, A.J., Oliveira, E., Jorge, A.M.: A multi-agent recommender system. In: Omatu, S., Paz Santana, J.F., González, S.R., Molina, J.M., Bernardos, A.M., Rodríguez, J.M.C. (eds.) Distributed Computing and Artificial Intelligence. AISC, vol. 151, pp. 281–288. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Marivate, V.N., Ssali, G., Marwala, T.: An Intelligent Multi-Agent Recommender System For Human Capacity Building. In: Proc. of the 14th IEEE Mediterranean Electrotechnical Conference, pp. 909–915 (2008)Google Scholar
  5. 5.
    Blanco-Fernández, Y., Pazos-Arias, J.J., Gil-Solla, A., Ramos-Cabrer, M., Barragáns-Martínez, B., López-Nores, M., García-Duque, J., Fernández-Vilas, A., Díaz-Redondo, R.P.: AVATAR: An Advanced Multi-agent Recommender System of Personalized TV Contents by Semantic Reasoning. In: Zhou, X., Su, S., Papazoglou, M.P., Orlowska, M.E., Jeffery, K. (eds.) WISE 2004. LNCS, vol. 3306, pp. 415–421. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Veltkamp, R.C., Hagedoorn, M.: Shape Similarity Measures, Properties and Constructions. In: Laurini, R. (ed.) VISUAL 2000. LNCS, vol. 1929, pp. 467–476. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Lesniewski, S.: On the foundations of set theory. Topoi 2, 7–52 (1982)Google Scholar
  8. 8.
    Polkowski, L., Artiemjew, P.: Granular Computing in the Frame of Rough Mereology. A Case Study: Classification of Data into Decision Categories by Means of Granular Reflections of Data. International Journal of Intelligent Systems 26(6), 555–571 (2011)CrossRefGoogle Scholar
  9. 9.
    Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative Filtering Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Linden, G., Smith, B., York, J.: Amazon.Com Recommendations: Item-To-Item Collaborative Filtering. IEEE Internet Computing 7(1) (2003)Google Scholar
  11. 11.
    Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.T.: Application of Dimensionality Reductio in Recommender System - A Case Study. In: Acm Webkdd Workshop (2000)Google Scholar
  12. 12.
    Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Koren, Y.: Tutorial on Recent Progress in Collaborative Filtering. In: Pu, P., Bridge, D.G., Mobasher, B., Ricci, F. (eds.) Proceedings of the 2008 Acm Conference on Recommender Systems, Recsys 2008, Lausanne, Switzerland, October 23-25, pp. 333–334 (2008)Google Scholar
  14. 14.
    Ettouney, R.S., Mjalli, F.S., Zaki, J.G., El-Rifai, M.A., Ettouney, H.M.: Forecasting Ozone Pollution using Artificial Neural Networks. Mgmt. Environ. Quality 20, 668–683 (2009)CrossRefGoogle Scholar
  15. 15.
    Abdul-Wahab, S., Bouhamra, W., Ettouney, H., Sowerby, B., Crittenden, B.D.: Predicting Ozone Levels: A Statistical Model for Predicting Ozone Levels. Environ. Sci. Pollut. Res. 3, 195–204 (1996)CrossRefGoogle Scholar
  16. 16.
    Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough Sets. Communications of the ACM 38(11), 88–95 (1995)CrossRefGoogle Scholar
  17. 17.
    Breese, J.S., Heckerman, D., Kadie, C.M.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: UAI, Technical report MSR-TR-98-12, pp. 43–52 (1998)Google Scholar
  18. 18.
    UCI ML Repository Datasets, http://www.ics.uci.edu/~mlearn/databases/

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mahmood A. Mahmood
    • 1
    • 2
  • Nashwa El-Bendary
    • 3
  • Jan Platoš
    • 4
  • Aboul Ella Hassanien
    • 5
    • 2
  • Hesham A. Hefny
    • 1
    • 2
  1. 1.ISSR, Computer Sciences and Information Dept.Cairo UniversityCairoEgypt
  2. 2.Scientific Research Group in Egypt (SRGE)GizaEgypt
  3. 3.Arab Academy for Science, Technology, and Maritime TransportCairoEgypt
  4. 4.Department of Computer ScienceVSB-Technical University of OstravaOstravaCzech Republic
  5. 5.Information Technology Dept., Faculty of Computers and InformationCairo UniversityCairoEgypt

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