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A study on the role of flexible preferences in group recommendations

  • Sriharsha Dara
  • C. Ravindranath ChowdaryEmail author
Article
  • 41 Downloads

Abstract

As online group activities have increased exponentially, the need for group recommender systems has also increased profoundly. The majority of the recommender systems are designed to recommend to user groups using fixed size preferences of the users. This paper examines the importance of flexible size user preferences in group recommender systems. We propose a variable size preference model in group recommendations both by considering the order in the preferences and also without considering the order. We also study the effect of variable size preferences in group recommendations. Experimental results show that our proposed flexible preference model has increased the overall group satisfaction to a great extent.

Keywords

Group recommender systems Ordered preferences Flexible preferences 

Notes

References

  1. 1.
    Agarwal A, Chakraborty M, Chowdary CR (2017) Does order matter? Effect of order in group recommendation. Expert Syst Appl 82(Supplement C):115–127CrossRefGoogle Scholar
  2. 2.
    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. ACM, New York, pp 119–126Google Scholar
  3. 3.
    Cantador I, Brusilovsky P, Kuflik T (2011) 2nd workshop on information heterogeneity and fusion in recommender systems (hetrec 2011). In: Proceedings of the 5th ACM conference on recommender systems, RecSys 2011. ACM, New YorkGoogle Scholar
  4. 4.
    Castro J, Lu J, Zhang G, Dong Y, Martínez L (2018) Opinion dynamics-based group recommender systems. IEEE Trans Syst Man Cybern Syst, 1–13Google Scholar
  5. 5.
    Crossen A, Budzik J, Hammond KJ (2002) Flytrap: Intelligent group music recommendation. In: Proceedings of the 7th international conference on intelligent user interfaces, IUI ’02. ACM, New York , pp 184–185Google Scholar
  6. 6.
    Delic A, Neidhardt J, Nguyen TN, Ricci F (2018) An observational user study for group recommender systems in the tourism domain. Inf Technol Tourism 19(1):87–116CrossRefGoogle Scholar
  7. 7.
    Guo J, Sun L, Li W, Yu T (2018) Applying uncertainty theory to group recommender systems taking account of experts preferences. Multimed Tools Appl 77(10):12901–12918CrossRefGoogle Scholar
  8. 8.
    Guo Z, Tang C, Niu W, Fu Y, Xia H, Tang H (2017) Beyond the aggregation of its members—a novel group recommender system from the perspective of preference distribution. In: Li G, Ge Y, Zhang Z, Jin Z, Blumenstein M (eds) Knowledge science, engineering and management. Springer International Publishing, Cham, pp 359–370Google Scholar
  9. 9.
    Harper MF, Konstan JA (2015) The movielens datasets: history and context. ACM Trans Interact Intell Syst 5(4):19,1–19,19CrossRefGoogle Scholar
  10. 10.
    Kagita VR, Pujari AK, Padmanabhan V (2015) Virtual user approach for group recommender systems using precedence relations. Inf Sci 294:15–30. Innovative Applications of Artificial Neural Networks in EngineeringCrossRefzbMATHGoogle Scholar
  11. 11.
    Kim H-N, El Saddik A (2015) A stochastic approach to group recommendations in social media systems. Inf Syst 50(Supplement C):76–93CrossRefGoogle Scholar
  12. 12.
    Kim JK, Kim HK, Young HO, Ryu YU (2010) A group recommendation system for online communities. Int J Inf Manag 30(3):212–219CrossRefGoogle Scholar
  13. 13.
    Kuhn HW (1955) The hungarian method for the assignment problem. Naval Res Logist Quart 2(1-2):83–97MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    McCarthy JF, Anagnost TD (1998) Musicfx: an arbiter of group preferences for computer supported collaborative workoutsGoogle Scholar
  15. 15.
    Nguyen TN (2017) Conversational group recommender systems. In: Proceedings of the 25th conference on user modeling, adaptation and personalization, UMAP ’17. ACM, New York, pp 331–334Google Scholar
  16. 16.
    Nguyen TN, Ricci F (2017) A chat-based group recommender system for tourism. In: Schegg R, Stangl B (eds) Information and communication technologies in tourism 2017. Springer International Publishing, Cham, pp 17–30Google Scholar
  17. 17.
    Ntoutsi E, Stefanidis K, Nørvåg K, Kriegel H-P (2012) Fast group recommendations by applying user clustering. In: International conference on conceptual modeling. Springer, p 126–140Google Scholar
  18. 18.
    Ortega F, Hernando A, Bobadilla J, Kang JH (2016) Recommending items to group of users using matrix factorization based collaborative filtering. Inform Sci 345:313–324CrossRefGoogle Scholar
  19. 19.
    Quijano-Sanchez L, Sauer C, Recio-Garcia JA, Diaz-Agudo B (2017) Make it personal: a social explanation system applied to group recommendations. Expert Syst Appl 76(Supplement C):36–48CrossRefGoogle Scholar
  20. 20.
    Recalde L (2017) A social framework for set recommendation in group recommender systems. In: Jose J et al. (eds) Advances in information retrieval, ECIR. Springer International Publishing, Cham, pp 735–743Google Scholar
  21. 21.
    Basu Roy S, Thirumuruganathan S, Amer-Yahia S, Das G, Yu C (2014) Exploiting group recommendation functions for flexible preferences. In: 2014 IEEE 30th international conference on data engineering, pp 412–423Google Scholar
  22. 22.
    Salehi-Abari A, Boutilier C (2015) Preference-oriented social networks: group recommendation and inference. In: Proceedings of the 9th ACM conference on recommender systems, RecSys ’15. ACM, New York, pp 35–42Google Scholar
  23. 23.
    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 351–352Google Scholar
  24. 24.
    Skowron P, Faliszewski P, Lang J (2016) Finding a collective set of items: from proportional multirepresentation to group recommendation, vol 241Google Scholar
  25. 25.
    Ureña R, Chiclana F, Fujita H, Herrera-Viedma E (2015) Confidence-consistency driven group decision making approach with incomplete reciprocal intuitionistic preference relations. Knowl-Based Syst 89:86–96CrossRefGoogle Scholar
  26. 26.
    Wang H, Shao S, Zhou X, Wan C, Bouguettaya A (2016) Preference recommendation for personalized search. Knowl-Based Syst 100:124–136CrossRefGoogle Scholar
  27. 27.
    Wang W, Zhang G, Lu J (2016) Member contribution-based group recommender system. Decis Support Syst 87:80–93CrossRefGoogle Scholar
  28. 28.
    Wu J, Chiclana F, Fujita H, Herrera-Viedma E (2017) A visual interaction consensus model for social network group decision making with trust propagation. Knowl-Based Syst 122:39–50CrossRefGoogle Scholar
  29. 29.
    Xu X, Yin X, Chen X (2019) A large-group emergency risk decision method based on data mining of public attribute preferences. Knowl-Based Syst 163:495–509CrossRefGoogle Scholar
  30. 30.
    Dara S, Chowdary CR, Kumar C (2019) A survey on group recommender systems. J Intell Inf Syst.  https://doi.org/10.1007/s10844-018-0542-3

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

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