MAICBR: A Multi-agent Intelligent Content-Based Recommendation System

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 10)


This study aims at proposing an intelligent and adaptive mechanism deploying intelligent agents for solving new user and overspecialization problems that exist in Content Based Recommendation (CBR) systems. Since the system is designed using software agents (SAs), it ensures highly desired full automation in web recommendations. The proposed system has been evaluated and the results suggested that there is an improvement in positive feedback rate and the decrease in recommendation rate.


Content Ontology Overspecialization New user problem Recommendation Semantic Software agents 


  1. 1.
    Singh, A., Sharma, A., Dey, N.: Semantics and agents oriented web personalization –state of the art. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 6(2), 35–49 (2015)Google Scholar
  2. 2.
    Park, D. H., Kim, H. K., Choi, I. Y., Kim, J. K.: A literature review and classification of recommender systems research. Expert Syst. App., 39, 10059–10072 (2012)Google Scholar
  3. 3.
    Anand, S. S., Mobasher, B.: Intelligent Techniques for Web Personalization. In: Intelligent Techniques for Web Personalization, Springer, 1–36 (2005)Google Scholar
  4. 4.
    Sarwar, B. M., Konstan, J. A., Borchers, N., HerIocker, J., Miller, B. Miller, Riedl, J.: Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System, In: Proceedings in CSCW’98 Seattle, Washington, USA, 345–354 (1998)Google Scholar
  5. 5.
    Debnath, S., Ganguly, N., Mitra P.: Feature Weighting in Content Based Recommendation System Using Social Network Analysis, In: Proceedings in WWW 2008, Beijing, China, 1041–1042 (2008)Google Scholar
  6. 6.
    Eirinaki, M., Vazirgiannis, M., Varlamis, I.: SEWeP: Using Site Semantics and a Taxonomy to Enhance the Web Personalization Process, In: Proceedings in SIGKDD ’03, Washington DC, USA, 99–108 (2003)Google Scholar
  7. 7.
    Singh, A. Juneja, D., Sharma, A. K.: Design of an intelligent and adaptive mapping mechanism for multiagent Interface. In: proceedings in International Conference on High Performance Architecture and Grid Computing (HPAGC’11), 373–384 (2011)Google Scholar
  8. 8.
    Albayrak, S., Wollny, S., Varone, N., Lommatzsch, A., Milosevic, D.: Agent Technology for Personalized Information Filtering: The PIA-System. In: ACM Symposium on Applied Computing, 54–59 (2005)Google Scholar
  9. 9.
    Miao, C, Yang Q., Fang H., Goh. A.: A cognitive approach for agent-based personalized recommendation. Knowl-Based Syst., 20(4), 397–405 (2007)Google Scholar
  10. 10.
    Huang, L., Dai, L., Wei, Y., Huang, M.: A personalized recommendation system based on multi-Agent. In: Proceedings in Second International Conference on Genetic and Evolutionary Computing, 223–226 (2008)Google Scholar
  11. 11.
    Pan, P., Wang, C., Horng, G., Cheng, S.: The Development of an ontology-based adaptive personalized recommender system, In: Proceedings in International Conference on Electronics and Information Engineering (ICEIE 2010), vol. 1, V176–V180 (2010)Google Scholar
  12. 12.
    Ge, J., Chen, Z., Peng, J., Li, T.: An ontology-based method for personalized recommendation. In: Proceedings in 11th IEEE Int. Conf. on Cognitive Informatics & Cognitive Computing, 522–526 (2012)Google Scholar
  13. 13.
    Blanco-Fernández, Y., López-Nores, M., Gil-Solla, A., Ramos-Cabrer, M., Pazos-Arias, J. J.: Exploring synergies between content-based filtering and spreading activation techniques in knowledge-based recommender systems. Inform Sciences, 181(21), 4823–4846 (2011)Google Scholar
  14. 14.
    Kahara, T., Haataja, K., Toivanen, P.: Towards more accurate and intelligent recommendation Systems. In: Proceedings in 13th International Conference on Intelligent Systems Design and Applications (ISDA), 165–171 (2013)Google Scholar
  15. 15.
    Maleszka, M., Mianowska, B., Nguyen, N. T.: A method for collaborative recommendation using knowledge integration tools and hierarchical structure of user profiles. Knowl-Based Syst., 47, 1–13 (2013)Google Scholar
  16. 16.
    Han, J., M. Kamber: Data Mining: Concepts and Techniques, 2nd edition, Morgan Kaufmann Publisher, ISBN 1-55860-901-6. (2006)Google Scholar
  17. 17.
    Rocha, C., Schawabe, D., Poggi, M.: A hybrid approach for searching in the semantic web. In: Proceedings in 13th International World Wide Web Conference (WWW-04), 74–84 (2004)Google Scholar
  18. 18.
    Anyanwu, K., Sheth A.: ρ-Queries: enabling querying for semantic associations on the semantic web. In: 12th International World Wide Web Conference (WWW-03), 115–125 (2003)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.MMICT & BMMaharishi Markandeshwer UniversityHaryanaIndia

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