Abstract
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.
Chapter PDF
Similar content being viewed by others
References
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)
Fransesco, R., Lior, R., Brach, S.: Introduction to Recommender Systems Handbook. Recommender Systems Handbook. Springer (2011)
Peter, B., Alfred, K., Wolfgang, N.: The Adaptive Web (2007)
Sharma, S.C.: Introductory Operation Research. Discovery Publishing House (2006)
Alexander, S.: Combinatorial Optimization. Springer (2003)
Gambetta, D.: Can We Trust Trust? Trust: Making and Breaking and Breaking Cooperative Relations (2000)
Qiu, Q., Annika, H.: Trust Based Recommendations for mobile Tourists in TIP. Hamiltou: [s.n.] (2008)
Fan, W.H., Cheng-Ting, W.: A strategy oriented operation module for recommender systems in E-commerce. Computers nad Operations Research (2010)
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)
Saul, V., Pablo, C.: Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems
Messa, P., Avesani, P.: Trust aware bootsraping of recommender systems. In: Proceedings of ECAI 2006 Workshop on Recommender Systems, pp. 29–32 (2006)
Chen, L.-S., Hsu, F.-H., Chen, M.-C., Hsu, Y.-C.: Developing recommender systems with the consideration of product profitability for sellers (2008)
Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and Metrics for Cold-Start Recommendations (2002)
Lashkari, Y., Metral, M., Maes, P.: Collaborative Interface Agents (1994)
Gonzalez, T.F.: Handbook of Approximation Algorithms and Metaheuristics (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Asikis, T., Lekakos, G. (2014). Operations Research and Recommender Systems. In: Yamamoto, S. (eds) Human Interface and the Management of Information. Information and Knowledge in Applications and Services. HIMI 2014. Lecture Notes in Computer Science, vol 8522. Springer, Cham. https://doi.org/10.1007/978-3-319-07863-2_55
Download citation
DOI: https://doi.org/10.1007/978-3-319-07863-2_55
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07862-5
Online ISBN: 978-3-319-07863-2
eBook Packages: Computer ScienceComputer Science (R0)