Skip to main content
SpringerLink
Log in

Preference relation based collaborative filtering with graph aggregation for group recommender system

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Most of the group recommender systems (GRS) apply some aggregation strategy to the ratings given by the group members for generating recommendations. But this can be highly influenced by a few members of the group, which can lead to poor group recommendation. Further, rating based aggregation strategies do not provide efficient ranking of items. Keeping these things in mind, this paper proposes a preference relation (PR) based GRS, that uses matrix factorization (MF) for predicting unknown PRs for group members. The aggregation of preferences is done using a novel virtual user based weight aggregation strategy. The weight aggregation concept is derived from the graph aggregation process. The advantage of this process is that it does not ignore weak preferences and also contributes towards group recommendation. The proposed model is evaluated and compared using standard ranking measures for MovieLens and NetFlix datasets. Experimental results obtained using Top-K recommendation task indicates the superiority of the proposed GRS method over the others. The proposed GRS model provides the best performance when we balance the number of member in a group and the number of recommended items.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Airiau S, Endriss U, Grandi U, Porello D, Uckelman J (2011) Aggregating dependency graphs into voting agendas in multi-issue elections. In: Twenty-second international joint conference on artificial intelligence

  2. Amer-Yahia S, Roy SB, Chawlat A, Das G, Yu C (2009) Group recommendation: Semantics and efficiency. Proc VLDB Endow 2(1):754–765. https://doi.org/10.14778/1687627.1687713

    Article  Google Scholar 

  3. Basile L (1996) Deleting inconsistencies in nontransitive preference relations. Int J Intell Sys 11 (5):267–277

    Article  MATH  Google Scholar 

  4. Bell R, Koren Y, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37. https://doi.org/10.1109/MC.2009.263

    Article  Google Scholar 

  5. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Sys 46:109–132. https://doi.org/10.1016/j.knosys.2013.03.012

    Article  Google Scholar 

  6. Brun A, Hamad A, Buffet O, Boyer A (2010) Towards preference relations in recommender systems. In: Workshop on Preference Learning, European Conference on Machine Learning and Principle and Practice of Knowledge Discovery in Databases (ECML-PKDD 2010), vol 51

  7. Castro J, Lu J, Zhang G, Dong Y, Martínez L. (2018) Opinion dynamics-based group recommender systems. IEEE Transactions on Systems Man, and Cybernetics: Systems 48(12):2394–2406. https://doi.org/10.1109/TSMC.2017.2695158

    Article  Google Scholar 

  8. Castro J, Quesada FJ, Palomares I, Martínez L. (2015) A consensus-driven group recommender system. Int J Intell Sys 30(8):887–906. https://doi.org/10.1002/int.21730

    Article  Google Scholar 

  9. Chapelle O, Metlzer D, Zhang Y, Grinspan P (2009) Expected reciprocal rank for graded relevance. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM ’09. ACM, New York, pp 621–630. https://doi.org/10.1145/1645953.1646033

  10. DeGroot MH (1974) Reaching a consensus. J Am Stat Assoc 69(345):118–121

    Article  MATH  Google Scholar 

  11. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

  12. Desarkar MS, Sarkar S, Mitra P (2010) Aggregating preference graphs for collaborative rating prediction. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys ’10. ACM, New York, pp 21–28, DOI https://doi.org/10.1145/1864708.1864716, (to appear in print)

  13. Desarkar MS, Sarkar S, Mitra P (2016) Preference relations based unsupervised rank aggregation for metasearch. Expert Syst Appl 49:86–98. https://doi.org/10.1016/j.eswa.2015.12.005

    Article  Google Scholar 

  14. Desarkar MS, Saxena R, Sarkar S (2012) Preference relation based matrix factorization for recommender systems. In: User modeling, adaptation, and personalization. Springer, Berlin, pp 63–75

  15. Dunn OJ (1961) Multiple comparisons among means. J Am Stat Assoc 56(293):52–64

    Article  MathSciNet  MATH  Google Scholar 

  16. Endriss U, Grandi U (2017) Graph aggregation. Artif Intell 245:86–114

    Article  MathSciNet  MATH  Google Scholar 

  17. Felfernig A, Boratto L, Stettinger M, Tkalčič M. (2018) Evaluating group recommender systems. Springer International Publishing, Cham, pp 59–71

    Book  Google Scholar 

  18. Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics 11(1):86–92

    Article  MathSciNet  MATH  Google Scholar 

  19. Guo Z, Zeng W, Wang H, Shen Y (2019) An enhanced group recommender system by exploiting preference relation. IEEE Access 7:24852–24864. https://doi.org/10.1109/ACCESS.2019.2897760

    Article  Google Scholar 

  20. Hammou BA, Lahcen AA, Mouline S (2019) A distributed group recommendation system based on extreme gradient boosting and big data technologies. Appl Intell 49(12):4128–4149. https://doi.org/10.1007/s10489-019-01482-9

    Article  Google Scholar 

  21. Harper FM, Konstan JA (2015) The movielens datasets: History and context. ACM Trans Interact Intell Syst 5(4):19:1–19:19. https://doi.org/10.1145/2827872

    Article  Google Scholar 

  22. Jameson A (2004) More than the sum of its members: Challenges for group recommender systems. In: Proceedings of the Working Conference on Advanced Visual Interfaces, AVI ’04. ACM, New York, pp 48–54

  23. Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of ir techniques. ACM Trans Inf Syst 20(4):422–446. https://doi.org/10.1145/582415.582418

    Article  Google Scholar 

  24. Kim HN, Rawashdeh M, El Saddik A (2013) Tailoring recommendations to groups of users: A graph walk-based approach. In: Proceedings of the 2013 International Conference on Intelligent User Interfaces, IUI ’13. https://doi.org/10.1145/2449396.2449401. Association for Computing Machinery, New York, pp 15–24

  25. Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788. https://doi.org/10.1038/44565

    Article  MATH  Google Scholar 

  26. Liu NH (2013) Comparison of content-based music recommendation using different distance estimation methods. Appl Intell 38(2):160–174

    Article  Google Scholar 

  27. Liu Q, Chen E, Xiong H, Ding CHQ, Chen J (2012) Enhancing collaborative filtering by user interest expansion via personalized ranking. IEEE Transactions on Systems, Man, and Cybernetics Part B (Cybernetics) 42(1):218–233. https://doi.org/10.1109/TSMCB.2011.2163711

    Article  Google Scholar 

  28. Liu S, Li G, Tran T, Jiang Y (2017) Preference relation-based Markov random fields for recommender systems. Mach Learn 106(4):523–546. https://doi.org/10.1007/s10994-016-5603-7

    Article  MathSciNet  MATH  Google Scholar 

  29. Lops P, de Gemmis M, Semeraro G (2011) Content-based recommender systems: state of the art and trends. Springer, Boston, pp 73–105. https://doi.org/10.1007/978-0-387-85820-3_3

    Book  Google Scholar 

  30. Luo X, Zhou M, Li S, You Z, Xia Y, Zhu Q (2016) A nonnegative latent factor model for large-scale sparse matrices in recommender systems via alternating direction method. IEEE Trans Neural Netw Learning Sys 27(3):579–592. https://doi.org/10.1109/TNNLS.2015.2415257

    Article  MathSciNet  Google Scholar 

  31. Luo X, Zhou M, Xia Y, Zhu Q (2014) An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Transactions on Industrial Informatics 10 (2):1273–1284. https://doi.org/10.1109/TII.2014.2308433

    Article  Google Scholar 

  32. Mao M, Lu J, Zhang G, Zhang J (2017) Multirelational social recommendations via multigraph ranking. IEEE Trans Cyberne 47(12):4049–4061. https://doi.org/10.1109/TCYB.2016.2595620

    Article  Google Scholar 

  33. Masthoff J (2011) Group recommender systems: combining individual models. Springer, Boston, pp 677–702

    Book  Google Scholar 

  34. McCarthy K, Salamó M, Coyle L, McGinty L, Smyth B, Nixon P (2006) Group recommender systems: A critiquing based approach. In: Proceedings of the 11th International Conference on Intelligent User Interfaces, IUI ’06. ACM, New York, pp 267–269

  35. Ortega F, Hernando A, Bobadilla J, Kang JH (2016) Recommending items to group of users using matrix factorization based collaborative filtering. Inform Sci 345:313–324. https://doi.org/10.1016/j.ins.2016.01.083

    Article  Google Scholar 

  36. Pazzani MJ, Billsus D (2007) Content-based recommendation systems. Springer, Berlin, pp 325–341

    Google Scholar 

  37. Pujahari A, Padmanabhan V (2015) A new grouping method based on social choice strategies for group recommender system. In: Computational Intelligence in data mining - volume 1. Springer, New Delhi, pp 325–332

  38. Pujahari A, Sisodia DS (2019) Modeling side information in preference relation based restricted boltzmann machine for recommender systems. Inform Sci 490:126–145

    Article  Google Scholar 

  39. Quijano-Sanchez L, Recio-Garcia JA, Diaz-Agudo B (2014) An architecture and functional description to integrate social behaviour knowledge into group recommender systems. Appl Intell 40(4):732–748. https://doi.org/10.1007/s10489-013-0504-y

    Article  Google Scholar 

  40. Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40(3):56–58. https://doi.org/10.1145/245108.245121

    Article  Google Scholar 

  41. Vo TV, Soh H (2018) Generation meets recommendation: Proposing novel items for groups of users. In: Proceedings of the 12th ACM Conference on Recommender Systems, RecSys ’18. ACM, New York, pp 145–153

  42. Wang W, Zhang G, Lu J (2016) Member contribution-based group recommender system. Decis Support Syst 87:80–93

    Article  Google Scholar 

  43. Wang X, Liu Y, Lu J, Xiong F, Zhang G (2019) Trugrc: Trust-aware group recommendation with virtual coordinators. Futur Gener Comput Syst 94:224–236. https://doi.org/10.1016/j.future.2018.11.030

    Article  Google Scholar 

  44. Xiao T, Shen H (2019) Neural variational matrix factorization for collaborative filtering in recommendation systems. Appl Intell 49(10):3558–3569

    Article  Google Scholar 

  45. Zhang T (2004) Solving large scale linear prediction problems using stochastic gradient descent algorithms. In: Proceedings of the Twenty-First International Conference on Machine Learning, ICML ’04. Association for Computing Machinery, New York, p 116, DOI https://doi.org/10.1145/1015330.1015332, (to appear in print)

  46. Zhang Z, Xu G, Zhang P, Wang Y (2017) Personalized recommendation algorithm for social networks based on comprehensive trust. Appl Intell 47(3):659–669

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Engineering, National Institute of Technology Raipur, GE Road, Raipur, Chhattisgarh, 492010, India

    Abinash Pujahari & Dilip Singh Sisodia

Authors
  1. Abinash Pujahari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dilip Singh Sisodia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abinash Pujahari.

Ethics declarations

Conflict of interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pujahari, A., Sisodia, D.S. Preference relation based collaborative filtering with graph aggregation for group recommender system. Appl Intell 51, 658–672 (2021). https://doi.org/10.1007/s10489-020-01848-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-020-01848-4

Keywords

Search

Navigation