A survey on group recommender systems

• 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.

  Article  Google Scholar 

• 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).

• 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.

• 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.

• 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.

• 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.

  Article  Google Scholar 

• 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.

  Article  Google Scholar 

• Boratto, L., Carta, S., Satta, M. (2010). Groups identification and individual recommendations in group recommendation algorithms. In PRSAT@ recsys (pp. 27–34).

• 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.

  Article  Google Scholar 

• 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.

  Article  Google Scholar 

• 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.

• 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.

  Article  Google Scholar 

• 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.

  Article  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 

• Christensen, I.A., & Schiaffino, S. (2011). Entertainment recommender systems for group of users. Expert Systems with Applications, 38(11), 14127–14135.

  Google Scholar 

• Colomer, J.M. (2013). Ramon llull: from ’ars electionis’ to social choice theory. Social Choice and Welfare, 40(2), 317–328.

  Article  MathSciNet  Google Scholar 

• 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.

• 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).

• Felfernig, A., Boratto, L., Stettinger, M., Tkalčič, M. (2018). Algorithms for group recommendation. Group Recommender Systems : An Introduction pp 27–58.

• Felfernig, A., Boratto, L., Stettinger, M., Tkalčič, M. (2018). Evaluating group recommender systems. Group Recommender Systems : An Introduction pp 59–71.

• Felfernig, A., Boratto, L., Stettinger, M., Tkalčič, M. (2018). Handling preferences. Group recommender systems : an introduction pp 91–103.

• 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.

  Article  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.

• Ghazarian, S., & Ali Nematbakhsh, M. (2015). Enhancing memory-based collaborative filtering for group recommender systems. Expert Systems with Applications, 42(7), 3801–3812.

  Article  Google Scholar 

• 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.

• 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.

• Jameson, A., & Smyth, B. (2007). Recommendation to groups. In The adaptive Web: methods and strategies of Web personalization (pp. 596–627).

• 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.

  Article  Google Scholar 

• Kim, H.-N., & El Saddik, A. (2015). A stochastic approach to group recommendations in social media systems. Information Systems, 50(Supplement C), 76–93.

  Article  Google Scholar 

• 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.

  Article  Google Scholar 

• Kompan, M., & Bielikova, M. (2014). Group recommendations: survey and perspectives. Computing and Informatics, 33(2), 446–476.

  Google Scholar 

• Lieberman, H., Dyke, N.V., Vivacqua, A. (1999). Let’s browse: a collaborative browsing agent. Knowledge-Based Systems, 12(8), 427–431.

  Article  Google Scholar 

• 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.

• Masthoff, J. (2011). Group recommender systems: combining individual models. In Recommender systems handbook (pp. 677–702). Berlin: Springer.

• 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.

• 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.

• McCarthy, J.F., & Anagnost, T.D. (1998). Musicfx: An arbiter of group preferences for computer supported collaborative workouts.

• 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).

• 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.

• Nguyen, T.N., & Ricci, F. (2017). Proceedings of the symposium on applied computing, SAC ’17 (pp. 1685–1692). New York: ACM.

• 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.

• 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.

  Article  Google Scholar 

• 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.

• 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.

  Article  Google Scholar 

• 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).

• 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.

  Article  Google Scholar 

• 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.

• 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.

  Article  Google Scholar 

• 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.

• 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.

• 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).

• Salamó, M., Mccarthy, K., Smyth, B. (2012). Generating recommendations for consensus negotiation in group personalization services. Personal and Ubiquitous Computing, 16(5), 597–610.

  Article  Google Scholar 

• 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.

• 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).

• 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.

• 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).

• 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).

• 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.

• 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.

• 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.

Download references