Nowadays, Recommender Systems (RS) are being widely and successfully used in online applications. A successful Recommender System can help in increasing the revenue of a web-site as well as helping it to maintain and increase its users. Until now, research in recommendation algorithms is mainly based on machine learning and AI techniques. In this article we aim to develop recommendation algorithms utilizing Operations Research (OR) methods that provide the ability to move towards an optimized set of items to be recommended. We focus on expressing the Collaborative Filtering Algorithm (CF or CFA) as a Greedy Construction Algorithm as well as implementing and testing a Collaborative Metaheuristic Algorithm (CMA) for providing recommendations. The empirical findings suggest that the recommendation problem can indeed be defined as an optimization problem, which provides new opportunities for the application of powerful and effective OR algorithms on recommendation problems.


Recommender Systems Personalization algorithms Operational Research Metaheuristic 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hosein, J., Hiang, S.A.T., Robab, S.: A Naive Recommendation Model for Large Databases. Internation Journal of Information an Education Technology, 216–219 (2012)Google Scholar
  2. 2.
    Fransesco, R., Lior, R., Brach, S.: Introduction to Recommender Systems Handbook. Recommender Systems Handbook. Springer (2011)Google Scholar
  3. 3.
    Peter, B., Alfred, K., Wolfgang, N.: The Adaptive Web (2007)Google Scholar
  4. 4.
    Sharma, S.C.: Introductory Operation Research. Discovery Publishing House (2006)Google Scholar
  5. 5.
    Alexander, S.: Combinatorial Optimization. Springer (2003)Google Scholar
  6. 6.
    Gambetta, D.: Can We Trust Trust? Trust: Making and Breaking and Breaking Cooperative Relations (2000)Google Scholar
  7. 7.
    Qiu, Q., Annika, H.: Trust Based Recommendations for mobile Tourists in TIP. Hamiltou: [s.n.] (2008)Google Scholar
  8. 8.
    Fan, W.H., Cheng-Ting, W.: A strategy oriented operation module for recommender systems in E-commerce. Computers nad Operations Research (2010)Google Scholar
  9. 9.
    Paolo, C., Franca, G., Roberto, T.: Investigating the Persuasion Potential of Recommender Systems from a Quality Perspective: An Empirical Study. ACM Transactions on Interactive Intelligent Systems 2 (2012)Google Scholar
  10. 10.
    Saul, V., Pablo, C.: Rank and Relevance in Novelty and Diversity Metrics for Recommender SystemsGoogle Scholar
  11. 11.
    Messa, P., Avesani, P.: Trust aware bootsraping of recommender systems. In: Proceedings of ECAI 2006 Workshop on Recommender Systems, pp. 29–32 (2006)Google Scholar
  12. 12.
    Chen, L.-S., Hsu, F.-H., Chen, M.-C., Hsu, Y.-C.: Developing recommender systems with the consideration of product profitability for sellers (2008)Google Scholar
  13. 13.
    Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and Metrics for Cold-Start Recommendations (2002)Google Scholar
  14. 14.
    Lashkari, Y., Metral, M., Maes, P.: Collaborative Interface Agents (1994)Google Scholar
  15. 15.
    Gonzalez, T.F.: Handbook of Approximation Algorithms and Metaheuristics (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Thomas Asikis
    • 1
  • George Lekakos
    • 1
  1. 1.Department of Management Science and TechnologyAthens University of Economics and BusinessAthensGreece

Personalised recommendations