A survey on group recommender systems

  • Sriharsha Dara
  • C. Ravindranath ChowdaryEmail author
  • Chintoo Kumar


Recommender systems are increasingly used in various domains like movies, travel, music, etc. The rise in social activities has increased the usage of recommender systems in general and group recommender systems in particular. A group recommender system is a system that recommends items to a group of users collectively, given their preferences. In addition to the user preferences, using social and behavioural aspects of group members to generate group recommendations will increase the quality of the content recommended in heterogeneous groups. Group recommender systems also address the cold start problem that arises in an individual recommendation system. This paper presents a survey on the state-of-the-art in group recommender systems concerning various domains. We discussed existing systems with respect to their aggregation and user preference models. This organisation is very useful to understand the intricacies with respect to each domain.


Group recommender systems Domain wise survey Aggregation models 


  1. Agarwal, A., Chakraborty, M., Ravindranath Chowdary, C. (2017). Does order matter? effect of order in group recommendation. Expert Systems with Applications, 82 (Supplement C), 115–127.CrossRefGoogle Scholar
  2. Ahmad, H. S., Nurjanah, D., Rismala, R. (2017). A combination of individual model on memory-based group recommender system to the books domain. In 5Th international conference on information and communication technology (ICoIC7) (pp. 1–6).Google Scholar
  3. Baltrunas, L., Makcinskas, T., Ricci, F. (2010). Group recommendations with rank aggregation and collaborative filtering. In Proceedings of the Fourth ACM conference on recommender systems, RecSys ’10 (pp. 119–126). New York: ACM.Google Scholar
  4. Baskin, J.P., & Krishnamurthi, S. (2009). Preference aggregation in group recommender systems for committee decision-making. In Proceedings of the Third ACM conference on recommender systems, RecSys ’09 (pp. 337–340). New York: ACM.Google Scholar
  5. Berkovsky, S., & Freyne, J. (2010). Group-based recipe recommendations: analysis of data aggregation strategies. In Proceedings of the Fourth ACM conference on recommender systems, RecSys ’10 (pp. 111–118). New York: ACM.Google Scholar
  6. Bobadilla, J., Ortega, F., Hernando, A., Bernal, J. (2012). Generalization of recommender systems Collaborative filtering extended to groups of users and restricted to groups of items. Expert Systems with Applications, 39(1), 172–186.CrossRefGoogle Scholar
  7. Boratto, L., & Carta, S. (2010). State-of-the-art in group recommendation and new approaches for automatic identification of groups. Information Retrieval and Mining in Distributed Environments, 324, 1–20.CrossRefGoogle Scholar
  8. Boratto, L., Carta, S., Satta, M. (2010). Groups identification and individual recommendations in group recommendation algorithms. In PRSAT@ recsys (pp. 27–34).Google Scholar
  9. Castro, J., Quesada, F.J., Palomares, I., Martínez, L. (2015). A consensus-driven group recommender system. International Journal of Intelligence Systems, 30(8), 887–906.CrossRefGoogle Scholar
  10. Castro, J., Yera, R., Martínez, L. (2017). An empirical study of natural noise management in group recommendation systems. Decision Support Systems, 94 (Supplement C), 1–11.CrossRefGoogle Scholar
  11. Chao, D.L., Balthrop, J., Forrest, S. (2005). Adaptive radio: achieving consensus using negative preferences. In Proceedings of the 2005 international ACM SIGGROUP conference on supporting group work, GROUP ’05 (pp. 120–123). New York: ACM.Google Scholar
  12. Chen, Y.Y., Cheng, A.J., Hsu, W.H. (2013). Travel recommendation by mining people attributes and travel group types from community-contributed photos. IEEE Transactions on Multimedia, 15(6), 1283–1295.CrossRefGoogle Scholar
  13. Chen, Y.-L., Cheng, L.-C., Chuang, C.-N. (2008). A group recommendation system with consideration of interactions among group members. Expert Systems with Applications, 34(3), 2082–2090.CrossRefGoogle Scholar
  14. Christensen, I.A, & Schiaffino, S. (2011). Entertainment recommender systems for group of users. Expert systems with applications, 38(11), 14127–14135.Google Scholar
  15. Christensen, I.A., & Schiaffino, S. (2011). Entertainment recommender systems for group of users. Expert Systems with Applications, 38(11), 14127–14135.Google Scholar
  16. Colomer, J.M. (2013). Ramon llull: from ’ars electionis’ to social choice theory. Social Choice and Welfare, 40(2), 317–328.MathSciNetCrossRefGoogle Scholar
  17. Crossen, A., Budzik, J., Hammond, K.J. (2002). Flytrap: intelligent group music recommendation. In Proceedings of the 7th international conference on intelligent user interfaces, IUI ’02 (pp. 184–185). New York: ACM.Google Scholar
  18. De Pessemier, T., Dhondt, J., Vanhecke, K., Martens, L. (2015). Travelwithfriends: a hybrid group recommender system for travel destinations. In Workshop on tourism recommender systems (touRS15), in conjunction with the 9th ACM conference on recommender systems (recsys 2015) (pp. 51–60).Google Scholar
  19. Felfernig, A., Boratto, L., Stettinger, M., Tkalčič, M. (2018). Algorithms for group recommendation. Group Recommender Systems : An Introduction pp 27–58.Google Scholar
  20. Felfernig, A., Boratto, L., Stettinger, M., Tkalčič, M. (2018). Evaluating group recommender systems. Group Recommender Systems : An Introduction pp 59–71.Google Scholar
  21. Felfernig, A., Boratto, L., Stettinger, M., Tkalčič, M. (2018). Handling preferences. Group recommender systems : an introduction pp 91–103.Google Scholar
  22. Garcia, I., Sebastia, L., Onaindia, E. (2011). On the design of individual and group recommender systems for tourism. Expert Systems with Applications, 38(6), 7683–7692.CrossRefGoogle Scholar
  23. 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.Google Scholar
  24. Ghazarian, S., & Ali Nematbakhsh, M. (2015). Enhancing memory-based collaborative filtering for group recommender systems. Expert Systems with Applications, 42(7), 3801–3812.CrossRefGoogle Scholar
  25. Gorla, J., Lathia, N., Robertson, S., Wang, J. (2013). Probabilistic group recommendation via information matching. In Proceedings of the 22Nd international conference on world wide Web, WWW ’13 (pp. 495–504). New York: ACM.Google Scholar
  26. Guzzi, F., Ricci, F., Burke, R. (2011). Interactive multi-party critiquing for group recommendation. In Proceedings of the Fifth ACM conference on recommender systems, RecSys ’11 (pp. 265–268). New York: ACM.Google Scholar
  27. Jameson, A., & Smyth, B. (2007). Recommendation to groups. In The adaptive Web: methods and strategies of Web personalization (pp. 596–627).Google Scholar
  28. Kaššák, O., Kompan, M., Bieliková, M. (2016). Personalized hybrid recommendation for group of users: top-n multimedia recommender. Information Processing & Management, 52(3), 459–477.CrossRefGoogle Scholar
  29. Kim, H.-N., & El Saddik, A. (2015). A stochastic approach to group recommendations in social media systems. Information Systems, 50(Supplement C), 76–93.CrossRefGoogle Scholar
  30. Kim, J.K., Kim, H.K., Oh, H.Y., Ryu, Y.U. (2010). A group recommendation system for online communities. International Journal of Information Management, 30 (3), 212–219.CrossRefGoogle Scholar
  31. Kompan, M., & Bielikova, M. (2014). Group recommendations: survey and perspectives. Computing and Informatics, 33(2), 446–476.zbMATHGoogle Scholar
  32. Lieberman, H., Dyke, N.V., Vivacqua, A. (1999). Let’s browse: a collaborative browsing agent. Knowledge-Based Systems, 12(8), 427–431.CrossRefGoogle Scholar
  33. Liu, X., Tian, Y., Ye, M., Lee, W.-C. (2012). Exploring personal impact for group recommendation. In Proceedings of the 21st ACM international conference on information and knowledge management, CIKM ’12 (pp. 674–683). New York: ACM.Google Scholar
  34. Masthoff, J. (2011). Group recommender systems: combining individual models. In Recommender systems handbook (pp. 677–702). Berlin: Springer.Google Scholar
  35. Mccarthy, J.F. (2002). Pocket restaurant finder: a situated recommender systems for groups. In Proceeding of workshop on mobile ad-hoc communication at the 2002 ACM conference on human factors in computer systems.Google Scholar
  36. Mccarthy, J.F. (2002). Pocket restaurantfinder: a situated recommender system for groups. In Workshop on mobile ad-hoc communication at the 2002 ACM conference on human factors in computer systems.Google Scholar
  37. McCarthy, J.F., & Anagnost, T.D. (1998). Musicfx: An arbiter of group preferences for computer supported collaborative workouts.Google Scholar
  38. McCarthy, K., Salamó, M., Coyle, L., McGinty, L., Smyth, B., Nixon, P. (2006). CATS A Synchronous approach to collaborative group recommendation. In Proceedings of the Nineteenth international Florida artificial intelligence research society conference, Melbourne Beach, Florida, USA, May 11–13, 2006 (pp. 86–91).Google Scholar
  39. Dery, L.N., Kalech, M., Rokach, L., Shapira, B. (2010). Iterative voting under uncertainty for group recommender systems. In Proceedings of the Fourth ACM conference on recommender systems, RecSys ’10 (pp. 265–268). New York: ACM.Google Scholar
  40. Nguyen, T.N., & Ricci, F. (2017). Proceedings of the symposium on applied computing, SAC ’17 (pp. 1685–1692). New York: ACM.Google Scholar
  41. O’Connor, M.J., Cosley, D., Konstan, J.A., Riedl, J. (2001). Polylens: a recommender system for groups of users. In ECSCW 2001: Proceedings of the Seventh European conference on computer supported cooperative work 16–20 September 2001, Bonn, Germany (pp. 199–218). Dordrecht: Springer.Google Scholar
  42. Ortega, F., Bobadilla, J., Hernando, A. , Gutiérrez, A. (2013). Incorporating group recommendations to recommender systems: alternatives and performance. Information Processing & Management, 49(4), 895–901.CrossRefGoogle Scholar
  43. Park, M.-H., Park, H.-S., Cho, S.-B. (2008). Restaurant recommendation for group of people in mobile environments using probabilistic multi-criteria decision making. In Proceedings of the 8th Asia-Pacific conference on computer-human interaction, APCHI ’08 (pp. 114–122). Berlin: Springer.Google Scholar
  44. Pera, M.S., & Ng, Y.-K. (2013). A group recommender for movies based on content similarity and popularity. Information Processing and Management, 49(3), 673–687.CrossRefGoogle Scholar
  45. Quijano-Sanchez, L., Recio-Garcia, J.A., Diaz-Agudo, B. (2010). Personality and social trust in group recommendations. In 2010 22Nd IEEE international conference on tools with artificial intelligence, (Vol. 2 pp. 121–126).Google Scholar
  46. Quijano-Sánchez, L., Díaz-agudo, B., Recio-garcía, J.A. (2014). Development of a group recommender application in a social network. Knowledge-Based Systems, 71(1), 72–85.CrossRefGoogle Scholar
  47. Quijano-Sánchez, L., Recio-garcía, J.A., Díaz-agudo, B., Jiménez-díaz. G. (2011). Happy movie: a group recommender application in facebook. In Proceedings of the twenty fourth international Florida artificial intelligence research society conference, FLAIRS 11 (pp. 419–420). Florida: AAAI Press.Google Scholar
  48. Quijano-Sanchez, L., Sauer, C., Recio-Garcia, J.A., Diaz-Agudo, B. (2017). Make it personal: a social explanation system applied to group recommendations. Expert Systems with Applications, 76(Supplement C), 36–48.CrossRefGoogle Scholar
  49. Rakesh, V., Lee, W.-C., Reddy, C.K. (2016). Probabilistic group recommendation model for crowdfunding domains. In Proceedings of the Ninth ACM international conference on Web search and data mining, WSDM ’16 (pp. 257–266). New York: ACM.Google Scholar
  50. Recio-Garcia, J.A., Jimenez-Diaz, G., Sanchez-Ruiz, A.A., Diaz-Agudo, B. (2009). Proceedings of the Third ACM conference on recommender systems, RecSys ’09 (pp. 325–328). New York: ACM.Google Scholar
  51. Basu Roy, S., Thirumuruganathan, S., Amer-Yahia, S., Das, G., Yu, C. (2014). Exploiting group recommendation functions for flexible preferences. In 30Th IEEE international conference on data engineering (pp. 412–423).Google Scholar
  52. Salamó, M., Mccarthy, K., Smyth, B. (2012). Generating recommendations for consensus negotiation in group personalization services. Personal and Ubiquitous Computing, 16(5), 597–610.CrossRefGoogle Scholar
  53. Salehi-Abari, A., & networks, C.B. (2015). Preference-oriented social group recommendation and inference. In Proceedings of the 9th ACM conference on recommender systems, RecSys ’15 (pp. 35–42). New York: ACM.Google Scholar
  54. Seko, S., Motegi, M., Yagi, T., Muto, S. (2011). Video content recommendation for group based on viewing history and viewer preference. In 2011 IEEE International conference on consumer electronics (ICCE) (pp. 351–352).Google Scholar
  55. Seko, S., Yagi, T., Motegi, M., Muto, S. (2011). Group recommendation using feature space representing behavioral tendency and power balance among members. In Proceedings of the Fifth ACM conference on recommender systems, RecSys ’11 (pp. 101–108). New York: ACM.Google Scholar
  56. Shi, J., Wu, B., Lin, X. (2015). A latent group model for group recommendation. In 2015 IEEE International conference on mobile services (pp. 233–238).Google Scholar
  57. Sotelo, R., Blanco, Y., Lopez, M., Gil, A., Pazos, J. (2009). Tv program recommendiation for groups based on multidimensional tv-anytime classifications. In 2009 Digest of technical papers international conference on consumer electronics (pp. 1–2).Google Scholar
  58. Wang, Xiaoyan, Sun, Lifeng, Wang, Zhi, Da, Meng. (2012). Group recommendation using external followee for social tv. In IEEE International conference on multimedia and expo (ICME) 2012 (pp. 37–42). Piscataway: IEEE.Google Scholar
  59. Ye, M., Liu, X., Lee, W.-C. (2012). Exploring social influence for recommendation: A generative model approach. In Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval, SIGIR ’12 (pp. 671–680). New York: ACM.Google Scholar
  60. Yuan, Q., Cong, G., Lin, C.-Y. (2014). Com: A generative model for group recommendation. In Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’14 (pp. 163–172). New York: ACM.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology (BHU)VaranasiIndia

Personalised recommendations