Social group recommendation in the tourism domain
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.
KeywordsSocial 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.
- 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
- 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
- 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
- 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
- Cantador, I., & Castells, P. (2012). Group recommender systems: New perspectives in the social web Vol. 32: Springer, Intelligent Systems Reference Library.Google Scholar
- Christensen, I.A., & Schiaffino, S. (2011). Entertainment recommender systems for group of users. Expert Systems with Applications, 38(11), 14127–14135.Google Scholar
- 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
- 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
- Friedkin, N., & Johnsen, E. (2011). Social influence network theory: a sociological examination of small group dynamics. Cambridge University Press.Google Scholar
- 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
- 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
- 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
- Ioannidis, S., Muthukrishnan, S., & Yan, J. (2013). A consensus-focused group recommender system. arXiv:1312.7076.
- 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
- Krulwich, B. (1997). Lifestyle finder: Intelligent user profiling using large-scale demographic data. AI Magazine, 18(2), 37–45.Google Scholar
- Masthoff, J. (2010). Recommender systems handbook. In Group recommender systems: Combining individual models (pp. 677–702). Springer.Google Scholar
- 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
- 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
- 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
- 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
- Schall, D. (2015). Social network-based recommender systems. Springer.Google Scholar
- 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
- 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
- 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
- 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