Towards a Generic Architecture for Recommenders Benchmarking

Mohamed Ramzi Haddad, Hajer Baazaoui, Djemel Ziou, Henda Ben Ghezala


With current growth of internet sales and content consumption, more research efforts are focusing on developing recommendation and personalization algorithms as a solution for the choice overload problem. In this paper, we first enumerate several state-of-the-art recommendation algorithms in order to highlight their main ideas and methodologies. Then, we propose a generic architecture for recommender systems benchmarking. Using the proposed architecture, we implement and evaluate several variants of existing recommendation algorithms and compare their results to our unified recommendation model. The experiments are conducted on a real world dataset in order to assess the genericity of our recommendation model and its quality. At the end, we conclude with some ideas for further development and research.


  1. Balabanovic, M. and Shoham, Y. (1997). Fab: contentbased, collaborative recommendation. Communications of the ACM, 40(3):66-72.
  2. Berkovsky, S., Kuflik, T., and Ricci, F. (2008). Mediation of user models for enhanced personalization in recommender systems. User Model. User-Adapt. Interact, 18(3):245-286.
  3. Bezdek, J. C. (1981). Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York.
  4. Boutemedjet, S. and Ziou, D. (2008). A graphical model for context-aware visual content recommendation. IEEE Transactions on Multimedia, 10(1):52-62.
  5. Burke, R. D. (2007). Hybrid web recommender systems. In Brusilovsky, P., Kobsa, A., and Nejdl, W., editors, The Adaptive Web, Methods and Strategies of Web Personalization, volume 4321 of Lecture Notes in Computer Science, pages 377-408. Springer.
  6. Deshpande, M. and Karypis, G. (2004). Item-based topN recommendation algorithms. ACM Transactions on Information Systems, 22(1):143-177.
  7. Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12):61-70.
  8. Haddad, M. R., Baazaoui, H., Ziou, D., and Ben Ghezala, H. (2012). Towards a new model for context-aware recommendation. In 6th IEEE International Conference Intelligent Systems (IS), Sofia, Bulgaria, pages 021 -027.
  9. Haddad, M. R., Baazaoui, H., Ziou, D., and Ghezala, H. B. (2014). A predictive model for recurrent consumption behavior: An application on phone calls. KnowledgeBased Systems, 64(0):32 - 43.
  10. Herlocker, L., J., Konstan, A., J., Borchers, A., and Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Theoretical Models, pages 230-237.
  11. Iyengar, S. S. and Lepper, M. R. (2000). When choice is demotivating: can one desire too much of a good thing? Journal of Personality and Social Psychology, 79(6):995-1006.
  12. Jacoby, J., Speller, D. E., and Berning, C. A. K. (1974). Brand choice behavior as a function of information load: Replication and extension. Journal of Consumer Research: An Interdisciplinary Quarterly, 1(1):33- 42.
  13. Kaufman, L. and Rousseeuw, P. J. (1990). Finding Groups in Data: an Introduction to Cluster Analysis. Wiley.
  14. Krulwich, B. (1997). Lifestyle finder: Intelligent user profiling using large-scale demographic data. AI Magazine, 18(2):37-45.
  15. Linden, G., Smith, B., and York, J. (2003). Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, 7(1):76-80.
  16. Mooney, R. J. and Roy, L. (2000). Content-based book recommending using learning for text categorization. In Proceedings of the Fifth ACM Conference on Digital Libraries, pages 195-204. ACM Press, New York.
  17. Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev, 13(5-6):393-408.
  18. Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., and Riedl, J. (1994). Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, pages 175-186, Chapel Hill, North Carolina. ACM.
  19. Sarwar, B. M., Karypis, G., Konstan, J. A., and Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. In WWW, pages 285-295.
  20. Schwartz, B. (2005). The paradox of choice: Why more is less. Harper Perennial.
  21. Woerndl, W. and Groh, G. (2007). Utilizing physical and social context to improve recommender systems. In Web Intelligence/IAT Workshops, pages 123-128. IEEE.

Paper Citation

in Harvard Style

Ramzi Haddad M., Baazaoui H., Ziou D. and Ben Ghezala H. (2015). Towards a Generic Architecture for Recommenders Benchmarking . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-074-1, pages 435-442. DOI: 10.5220/0005216904350442

in Bibtex Style

author={Mohamed Ramzi Haddad and Hajer Baazaoui and Djemel Ziou and Henda Ben Ghezala},
title={Towards a Generic Architecture for Recommenders Benchmarking},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},

in EndNote Style

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Towards a Generic Architecture for Recommenders Benchmarking
SN - 978-989-758-074-1
AU - Ramzi Haddad M.
AU - Baazaoui H.
AU - Ziou D.
AU - Ben Ghezala H.
PY - 2015
SP - 435
EP - 442
DO - 10.5220/0005216904350442