Dominance Weighted Social Choice Functions for Group Recommendations

  • Silvia Rossi
    Universita' degli Studi di Napoli "Federico II" silvia.rossi[at]
  • Francesco Barile
  • Antonio Caso
    Universita' degli Studi di Napoli "Federico II"


In travel domains, decision support systems provide support to tourists in the planning of their vacation. In particular, when the number of possible Points of Interest (POI) to visit is large, the system should help tourists providing recommendations on the POI that could be more interesting for them. Since traveling is, usually, an activity that involves small groups of people, the system should take simultaneously into account the preferences of each group's member. At the same time, it also should model possible intra-group relationships, which can have an impact in the group decision-making process. In this paper, we model this problem as a multi-agent aggregation of preferences by using weighted social choice functions, whereas such weights are automatically evaluated by analyzing the interactions of the group's members on Online Social Networks.
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Adali, S., Sisenda, F., and Magdon-Ismail, M., 2012. Actions Speak As Loud As Words: Predicting Relationships from Social Behavior Data. In Proc. of the 21st International Conference on World Wide Web, pages 689–698. ACM.

Ardissono, L., Goy, A., Petrone, G., Segnan, M., and Torasso, P., 2003. INTRIGUE: Personalized recommendation of tourist attractions for desktop and handset devices. Applied Artificial Intelligence, 17(8):687–714.

Banks, L. and Wu, S., 2009. All Friends Are Not Created Equal: An Interaction Intensity Based Approach to Privacy in Online Social Networks. In International Conference on Computational Science and Engineering, volume 4, pages 970–974.

Bekkerman, P., Kraus, S., and Ricci, F., 2006. Applying cooperative negotiation methodology to group recommendation problem. In Proceedings of Workshop on Recommender Systems in 17th European Conference on Artificial Intelligence (ECAI 2006), pages 72–75. Citeseer.

Berkovsky, S., Freyne, J., and Coombe, M., 2009. Aggregation Trade Offs in Family Based Recommendations. In Nicholson, A. and Li, X., editors, Advances in Artificial Intelligence, volume 5866 of Lecture Notes in Computer Science, pages 646–655. Springer Berlin Heidelberg.

Brin, S. and Page, L., 1998. The Anatomy of a Large-scale Hypertextual Web Search Engine. Comput. Netw. ISDN Syst., 30(1-7):107–117.

Cantador, I. and Castells, P., 2012. Group Recommender Systems: New Perspectives in the Social Web. In Recommender Systems for the Social Web, volume 32 of Intelligent Systems Reference Library, pages 139–157. Springer.

Carvalho, L. A. M. and Macedo, H. T., 2013a. Generation of Coalition Structures to Provide Proper Groups’ Formation in Group Recommender Systems. In Proceedings of the 22Nd International Conference on World Wide Web Companion, WWW ’13 Companion, pages 945–950. International World Wide Web Conferences Steering Committee.

Carvalho, L. A. M. C. and Macedo, H. T., 2013b. Users’ Satisfaction in Recommendation Systems for Groups: An Approach Based on Noncooperative Games. In Proc. of the 22Nd Int. Conf. on World Wide Web Companion, WWW ’13 Companion, pages 951–958.

Caso, A. and Rossi, S., 2014. Users Ranking in Online Social Networks to Support POIs Selection in Small Groups. In Extended Proceedings of the 22nd Conference on User Modelling, Adaptation and Peronalization - UMAP 2014, pages 5–8.

Garcia, I. and Sebastia, L., 2014. A negotiation framework for heterogeneous group recommendation. Expert Systems with Applications, 41(4):1245–1261.

Garcia, I., Sebastia, L., and Onaindia, E., 2009. A Negotiation Approach for Group Recommendation. In Proceedings of the 2009 International Conference on Artificial Intelligence, Las Vegas Nevada, USA, pages 919–925. CSREA Press.

Gartrell, M., Xing, X., Lv, Q., Beach, A., Han, R., Mishra, S., and Seada, K., 2010. Enhancing Group Recommendation by Incorporating Social Relationship Interactions. In Proc. of the 16th ACM International Conference on Supporting Group Work, pages 97–106. ACM.

Gilbert, E. and Karahalios, K., 2009a. Predicting Tie Strength with Social Media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’09, pages 211–220. ACM.

Gilbert, E. and Karahalios, K., 2009b. Predicting Tie Strength with Social Media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’09, pages 211–220. ACM, New York, NY, USA.

Jameson, A., 2004. More Than the Sum of Its Members: Challenges for Group Recommender Systems. In Proceedings of the Working Conference on Advanced Visual Interfaces, AVI ’04, pages 48–54. ACM.

Jelassi, M. T. and Foroughi, A., 1989. Negotiation support systems: an overview of design issues and existing software. Decision Support Systems, 5(2):167 – 181.

Langville, A. N. and Meyer, C. D., 2004. Deeper inside pagerank. Internet Mathematics, 1:2004.

Masthoff, J., 2011. Group Recommender Systems: Combining Individual Models. In Recommender Systems Handbook, pages 677–702. Springer US.

McCarthy, K., McGinty, L., Smyth, B., and Salamøs, M., 2006. The Needs of the Many: A Case-Based Group Recommender System. In Roth-Berghofer, T., GÃ?uker, M., and GÃijvenir, H., editors, Advances in Case-Based Reasoning, volume 4106 of Lecture Notes in Computer Science, pages 196–210. Springer Berlin Heidelberg.

Newman, M. E. J., 2004. Analysis of weighted networks. Phys. Rev. E, 70.

O’Connor, M., Cosley, D., Konstan, J. A., and Riedl, J., 2001. PolyLens: A Recommender System for Groups of Users. In Proc. of the 7th European Conf. on CSCW, pages 199–218.

Opsahl, T., Agneessens, F., and Skvoretz, J., 2010. Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3):245 – 251.

Salehi-Abari, A. and Boutilier, C., 2014. Empathetic Social Choice on Social Networks. In 13th International Conference on Autonomous Agents and Multiagent Systems, pages 693–700.

Seko, S., Yagi, T., Motegi, M., and Muto, S., 2011. Group Recommendation Using Feature Space Representing Behavioral Tendency and Power Balance Among Members. In Proc. of the Fifth ACM Conference on Recommender Systems, pages 101–108.

Senot, C., Kostadinov, D., Bouzid, M., Picault, J., Aghasaryan, A., and Bernier, C., 2010. Analysis of Strategies for Building Group Profiles. In User Modeling, Adaptation, and Personalization, volume 6075 of LNCS, pages 40–51.

Souffriau, W. and Vansteenwegen, P., 2010. Tourist Trip Planning Functionalities: State-of-the-Art and Future. In Current Trends in Web Engineering, volume 6385 of LNCS, pages 474–485.

Theodorson, G. A., 1957. The Relationship between Leadership and Popularity Roles in Small Groups. American Sociological Review, 22(1):pp. 58–67.
Rossi, S., Barile, F., & Caso, A. (2015). Dominance Weighted Social Choice Functions for Group Recommendations. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 4(1), 65–79.


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