Journal of Intelligent Information Systems

, Volume 47, Issue 2, pp 209–231 | Cite as

Social group recommendation in the tourism domain

  • Ingrid Christensen
  • Silvia Schiaffino
  • Marcelo Armentano


Recommender Systems learn users’ preferences and tastes in different domains to suggest potentially interesting items to users. Group Recommender Systems generate recommendations that intend to satisfy a group of users as a whole, instead of individual users. In this article, we present a social based approach for recommender systems in the tourism domain, which builds a group profile by analyzing not only users’ preferences, but also the social relationships between members of a group. This aspect is a hot research topic in the recommender systems area. In addition, to generate the individual and group recommendations our approach uses a hybrid technique that combines three well-known filtering techniques: collaborative, content-based and demographic filtering. In this way, the disadvantages of one technique are overcome by the others. Our approach was materialized in a recommender system named Hermes, which suggests tourist attractions to both individuals and groups of users. We have obtained promising results when comparing our approach with classic approaches to generate recommendations to individual users and groups. These results suggest that considering the type of users’ relationship to provide recommendations to groups leads to more accurate recommendations in the tourism domain. These findings can be helpful for recommender systems developers and for researchers in this area.


Social recommender systems Recommender systems Tourism 



We would like to thank Soledad Diez González and Matias Urrutia who developed the Hermes system.

This work has been partially funded by ANPCyT through project PICT 2011-0366.


  1. Ardissono, L., Goy, A., Petrone, G., Segnan, G., & Torasso, G. (2003). Intrigue personalized recommendation of tourist attractions for desktop and handset devices. In Applied artificial intelligence (pp. 687–714). Taylor and Francis.Google Scholar
  2. Avazpour, I., Pitakrat, T., Grunske, L., & Grundy, J. (2014). Recommendation systems in software engineering. In Dimensions and metrics for evaluating recommendation systems (pp. 245–273). Berlin: Springer.Google Scholar
  3. Billsus, D., & Pazzani, M.J. (2000). User modeling for adaptive news access. User Modeling and User-Adapted Interaction, 10, 147–180.CrossRefGoogle Scholar
  4. Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132.CrossRefGoogle Scholar
  5. Bonhard, P., Harries, C., McCarthy, J., & Sasse, M. (2006). Accounting for taste: using profile similarity to improve recommender systems. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1057–1066). ACM Press.Google Scholar
  6. Boratto, L., & Carta, S. (2011). State-of-the-art in group recommendation and new approaches for automatic identification of groups. In Soro, A., Vargiu, E., Armano, G., & Paddeu, G. (Eds.) Information retrieval and mining in distributed environments, volume 324 of studies in computational intelligence (pp. 1–20). Berlin: Springer.Google Scholar
  7. Borras, J., Moreno, A., & Valls, A. (2014). Intelligent tourism recommender systems: a survey. Expert Systems with Applications, 41(16), 7370–7389.CrossRefGoogle Scholar
  8. Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction, 12(4), 331–370.CrossRefzbMATHGoogle Scholar
  9. Cantador, I., & Castells, P. (2012). Group recommender systems: New perspectives in the social web Vol. 32: Springer, Intelligent Systems Reference Library.Google Scholar
  10. Castro, J., Quesada, F.J., Palomares, I., & Martínez, L. (2015). A consensus-driven group recommender system. International Journal of Intelligent Systems, 30(8), 887–906.CrossRefGoogle Scholar
  11. Christensen, I.A., & Schiaffino, S. (2011). Entertainment recommender systems for group of users. Expert Systems with Applications, 38(11), 14127–14135.Google Scholar
  12. Coyle, L., & Cunningham, P. (2004). Advances in case-based reasoning. In 7th European conference, ECCBR 2004, proceedings, volume 3155 of LNCS, chapter improving recommendation ranking by learning personal feature weights (pp. 560–572). Springer.Google Scholar
  13. Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., & Suri, S. (2008). Feedback effects between similarity and social influence in online communities. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 160–168).Google Scholar
  14. Crossen, A., Budzik, J., & Hammond, K.J. (2002). Flytrap: intelligent group music recommendation. In IUI ’02: Proceedings Of the 7th international conference on intelligent user interfaces (pp. 184–185). New York: ACM.CrossRefGoogle Scholar
  15. Friedkin, N., & Johnsen, E. (2011). Social influence network theory: a sociological examination of small group dynamics. Cambridge University Press.Google Scholar
  16. Garcia, I., Sebastia, L., Onaindia, E., & Guzman, C. (2009). A group recommender system for tourist activities. In Proceedings of the 10th international conference on e-commerce and web technologies, EC-web 2009 (pp. 26–37). Berlin: Springer.Google Scholar
  17. Gartrell, M., Xing, X., Lv, Q., Beach, A., Han, R., Mishra, S., & Seada, K. (2010). Enhancing group recommendation by incorporating social relationship interactions. In Proceedings of the 16th ACM international conference on supporting group work, GROUP ’10 (pp. 97–106). New York: ACM.CrossRefGoogle Scholar
  18. Hevner, A.R., March, S.T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.Google Scholar
  19. Ioannidis, S., Muthukrishnan, S., & Yan, J. (2013). A consensus-focused group recommender system. arXiv:1312.7076.
  20. Jameson, A., & Smyth, B. (2007). Recommendation to groups. In The adaptive web: Methods and strategies of web personalization, chapter 20 (pp. 596–627).Google Scholar
  21. Kaminskas, M., Fernández-tobías, I., Ricci, F., & Cantador, I. (2014). Knowledge-based identification of music suited for places of interest. Information Technology & Tourism, 14(1), 73–95.CrossRefGoogle Scholar
  22. Krulwich, B. (1997). Lifestyle finder: Intelligent user profiling using large-scale demographic data. AI Magazine, 18(2), 37–45.Google Scholar
  23. Linden, G., Smith, B., & York, J. (2003). recommendations - item-to-item collaborative filtering. IEEE Internet Computing, 7(1), 76–80.CrossRefGoogle Scholar
  24. Masthoff, J. (2010). Recommender systems handbook. In Group recommender systems: Combining individual models (pp. 677–702). Springer.Google Scholar
  25. McCarthy, J.F. (2002). Pocket restaurantfinder a situated recommender system for groups. In Proceedings of the workshop on mobile ad-hoc communication at the 2002 ACM conference on human factors in computer systems. Minneapolis: ACM.Google Scholar
  26. McCarthy, K., McGinty, L., Smyth, B., & Salamó, M. (2006). The needs of the many: a case-based group recommender system. In Proceedings of the 8th european conference on advances in case-based reasoning, ECCBR’06 (pp. 196–210). Berlin: Springer.CrossRefGoogle Scholar
  27. Noguera, J.M., Barranco, M.J., Segura, R.J., & Martínez, L. (2012). A mobile 3d-gis hybrid recommender system for tourism. Information Sciences, 215, 37–52.CrossRefGoogle Scholar
  28. O’Connor, M., Cosley, D., Konstan, J.A., & Riedl, J. (2001). Polylens a recommender system for groups of users. In ECSCW’01: Proceedings of the seventh conference on european conference on computer supported cooperative work (pp. 199–218). Norwell: Kluwer Academic Publishers.Google Scholar
  29. Pazzani, M., & Billsus, D. (2007). Content-based recommendation systems. In Brusilovsky, P., Kobsa, A., & Nejdl, W. (Eds.) The adaptive web, volume 4321 of lecture notes in computer science, chapter 10 (pp. 325–341). Berlin: Springer.Google Scholar
  30. Quijano-Sanchez, L., Recio-Garcia, J., Diaz-Agudo, B., & Jimenez-Diaz, G. (2013). Social factors in group recommender systems. ACM Transactions on Intelligent Systems and Technology, 4(1), 8:1–8:30.CrossRefGoogle Scholar
  31. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). Grouplens: an open architecture for collaborative filtering of netnews. In Proceedings of ACM 1994 conference on computer supported cooperative work (pp. 175–186). ACM Press.Google Scholar
  32. Schall, D. (2015). Social network-based recommender systems. Springer.Google Scholar
  33. Schiaffino, S., & Amandi, A. (2009). Building an expert travel agent as a software agent. Expert Systems with Applications, 36(2, Part 1), 1291–1299.CrossRefGoogle Scholar
  34. Sebastia, L., Giret, A., & Garcia, I. (2011). A multi agent architecture for single user and group recommendation in the tourism domain. International Journal of Artificial Intelligence, 6(11), 161–182.Google Scholar
  35. Shang, S., Hui, P., Kulkarni, S., & Cuff, P. (2011). Wisdom of the crowd: Incorporating social influence in recommendation models. In IEEE 17th international conference on parallel and distributed systems (ICPADS), 2011 (pp. 835–840).Google Scholar
  36. Srivihok, A., & Sukonmanee, P. (2005). E-commerce intelligent agent: personalization travel support agent using q learning. In Proceedings of the 7th international conference on electronic commerce. ICEC 2005 (pp. 287–292). ACM Press.Google Scholar
  37. Young, K., & Srivastava, J. (2007). Modeling information diffusion in implicit networks. In Proceedings of the 9th international conference on electronic commerce (pp. 293–302). ACM Press.Google Scholar
  38. Yu, Z., Zhou, X., Hao, Y., & Gu, J. (2006). Tv program recommendation for multiple viewers based on user profile merging. User Modeling and User-Adapted Interaction, 16(1), 63–82.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.ISISTAN (CONICET-UNCPBA)Campus UniversitarioTandilArgentina

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