Main Article Content

Christian Paulo Villavicencio
Silvia Schiaffino
J. Andrés Díaz-Pace
Ariel Monteserin
Vol. 5 No. 3 (2016), Articles, pages 1-12
Accepted: Nov 15, 2016
Copyright How to Cite


Providing recommendations to groups of users has become popular in many applications today. Although several group recommendation techniques exist, the generation of items that satisfy all group members in an even way still remains a challenge. To this end, we have developed a multi-agent approach called PUMAS-GR that relies on negotiation techniques to improve group recommendations. We applied PUMAS-GR to the movies domain, and used the monotonic concession protocol to reach a consensus on the movies proposed to a group.


Download data is not yet available.

Article Details


Blanco-Fernandez, Y. et al., 2004. AVATAR: An Advanced Multi-agent Recommender System of Personalized TV Contents by Semantic Reasoning. s.l., s.n., pp. 415-421.

Cantador, I. & Castells, P., 2012. Group Recommender Systems: New Perspectives in the Social Web. In: Recommender Systems for the Social Web. s.l.:Springer Berlin Heidelberg, pp. 139-157.

Christensen, I. & Schiaffino, S., 2014. A hybrid approach for group profiling in recommender systems. J. of Universal Computer Science, 20(4), pp. 507-533.

Endriss, U., 2006. Monotonic Concession Protocols for Multilateral Negotiation. New York, NY, USA, ACM, pp. 392-399.

Garcia, I. & Sebastia, L., 2014. A negotiation framework for heterogeneous group recommendation. Expert Systems with Applications , 41(4,1), pp. 1245-1261.

Garcia, I., Sebastia, L. & Onaindia, E., 2009. A Negotiation Approach for Group Recommendation. s.l., s.n., pp. 919-925.

Jameson, A. & Smyth, B., 2007. The Adaptive Web. In: P. Brusilovsky, A. Kobsa & W. Nejdl, eds. s.l.:Springer-Verlag, pp. 596-627.

Lee, W.-P., 2004. Towards agent-based decision making in the electronic marketplace: interactive recommendation and automated negotiation. s.l.:Elsevier {BV}.

Lopes, J. S., Alvarez-Napagao, S., Confalonieri, R. & Vázquez-Salceda, J., 2009. USE: a Multi-Agent User-Driven Recommendation System for Semantic Knowledge Extraction.

Marivate, V. N., Ssali, G. & Marwala, T., 2008. An Intelligent Multi-Agent Recommender System for Human Capacity Building. s.l., IEEE, pp. 909-915.

Masthoff, J., 2011. Recommender Systems Handbook. In: F. Ricci, L. Rokach, B. Shapira & P. Kantor, eds. s.l.:Springer Science+Business Media, pp. 677-702.

Morais, J., Oliveira, E. & Jorge, A., 2012. Distributed Computing and Artificial Intelligence. In: s.l.:Springer, pp. 281-288.

Ricci, F., Rokach, L., Shapira, B. & Kantor, P., 2010. Recommender Systems Handbook. s.l.:Springer.

Sebastiá, L., Giret, A. & García, I., 2011. A Multi Agent Architecture for Single User and Group Recommendation in the Tourism Domain.

Skocir, P., Marusic, L., Marusic, M. & Petric, A., 2012. Agent and Multi-Agent Systems. Technologies and Applications. In: s.l.:Springer, pp. 104-113.

Villavicencio, C., Schiaffino, S., Diaz-Pace, J. A. & Monteserin, A., 2016. PUMAS-GR: A Negotiation-Based Group Recommendation System for Movies. In: Y. Demazeau, T. Ito, J. Bajo & M. J. Escalona, eds. Advances in Practical Applications of Scalable Multi-agent Systems. The PAAMS Collection: 14th International Conference, PAAMS 2016, Sevilla, Spain, June 1-3, 2016, Proceedings. Cham: Springer International Publishing, pp. 294-298.

Wooldridge, M., 2009. An Introduction to MultiAgent Systems. Second Edition ed. s.l.:John Wiley & Sons.

Zeuthen, F. L. B., 1930. Problems of Monopoly and Economic Warfare. London, UK: Routledge and Sons.