Logo PTI Logo FedCSIS

Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 32

Evaluating Diversification in Group Recommender Systems


DOI: http://dx.doi.org/10.15439/2022F75

Citation: Communication Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 32, pages 4754 ()

Full text

Abstract. The formation of groups is an ordinary event in ourroutines. For example, people used to lunch, travel, or hang out in groups. Conversely, getting a consensus over an item may be difficult for some groups as the number of digital information increases. Group Recommender Systems (GRS) rise to assist in this task, as they filter which items may be more relevant to the group. Although there are consensus techniques to help in this matter, recommendations to groups can become monotonous, and this opens space for applying diversification techniques to improve recommendations. In this paper, we expose a model for recommendation to groups using diversification techniques and present the results of the online experiment where the proposal obtained an increase in precision at all levels compared with baseline.


  1. J. Masthoff, Group Recommender Systems: Combining Individual Models. Boston, MA: Springer US, 2011, pp. 677–702.
  2. S. Amer-Yahia, S. B. Roy, A. Chawlat, G. Das, and C. Yu, “Group recommendation: Semantics and efficiency,” Proc. VLDB Endow., vol. 2, no. 1, pp. 754–765, Aug. 2009. http://dx.doi.org/10.14778/1687627.1687713
  3. A. Jameson and B. Smyth, Recommendation to Groups. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 596–627.
  4. K. Bradley and B. Smyth, “Improving recommendation diversity,” in Proceedings of the Twelfth Irish Conference on Artificial Intelligence and Cognitive Science, Maynooth, Ireland. Citeseer, 2001, pp. 85–94.
  5. M. Kaminskas and D. Bridge, “Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-accuracy objectives in recommender systems,” ACM Trans. Interact. Intell. Syst., vol. 7, no. 1, pp. 2:1–2:42, Dec. 2016. http://dx.doi.org/10.1145/2926720
  6. N. T. Toan, P. T. Cong, N. T. Tam, N. Q. V. Hung, and B. Stantic, “Diversifying group recommendation,” IEEE Access, vol. 6, pp. 17 776–17 786, 2018. http://dx.doi.org/10.1109/ACCESS.2018.2815740
  7. A. Oliveira and F. Durao, “A group recommendation model using diversification techniques,” in Proceedings of the 54th Hawaii International Conference on System Sciences, Hawaii, HI, USA, 2021. http://dx.doi.org/10.24251/HICSS.2021.326 p. 2669.
  8. D. Qin, X. Zhou, L. Chen, G. Huang, and Y. Zhang, “Dynamic connection-based social group recommendation,” IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 3, pp. 453–467, 2020. http://dx.doi.org/10.1109/TKDE.2018.2879658
  9. Y. Zheng, “Educational group recommendations by learning group expectations,” in 2019 IEEE International Conference on Engineering, Technology and Education (TALE), 2019. http://dx.doi.org/10.1109/TALE48000.2019.9225968 pp. 1–7.
  10. D. Karimpour, M. A. Z. Chahooki, and A. Hashemi, “Grouprec: Group recommendation by numerical characteristics of groups in telegram,” in 2021 11th International Conference on Computer Engineering and Knowledge (ICCKE), 2021. http://dx.doi.org/10.1109/ICCKE54056.2021.9721494 pp. 115–120.
  11. S. Raza and C. Ding, “A regularized model to trade-off between accuracy and diversity in a news recommender system,” in 2020 IEEE International Conference on Big Data (Big Data), 2020. http://dx.doi.org/10.1109/BigData50022.2020.9378340 pp. 551–560.
  12. S. Miyamoto, T. Zamami, and H. Yamana, “Improving recommendation diversity across users by reducing frequently recommended items,” in 2018 IEEE International Conference on Big Data (Big Data), 2018. http://dx.doi.org/10.1109/BigData.2018.8622314 pp. 5392–5394.
  13. D. Bertram, “Likert scales,” Retrieved November, vol. 2, p. 2013, 2007.
  14. B. Smyth and P. McClave, “Similarity vs. diversity,” in Case-Based Reasoning Research and Development, D. W. Aha and I. Watson, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44593-5_25 pp. 347–361.
  15. C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, “Improving recommendation lists through topic diversification,” in Proceedings of the 14th International Conference on World Wide Web, ser. WWW ’05. New York, NY, USA: ACM, 2005. http://dx.doi.org/10.1145/1060745.1060754 p. 22–32.
  16. A. G. Galliano, Ed., Introdução à sociologia. Harper Row do Brasil, 1981.
  17. O. Kaššák, M. Kompan, and M. Bieliková, “Personalized hybrid recommendation for group of users: Top-n multimedia recommender,” Information Processing Management, vol. 52, no. 3, pp. 459 – 477, 2016. http://dx.doi.org/10.1016/j.ipm.2015.10.001
  18. R. Ahuja, A. Solanki, and A. Nayyar, “Movie recommender system using k-means clustering and k-nearest neighbor,” in 2019 9th International Conference on Cloud Computing, Data Science Engineering (Confluence), 2019. http://dx.doi.org/10.1109/CONFLUENCE.2019.8776969 pp. 263–268.
  19. S. Girase, D. Mukhopadhyay et al., “Role of matrix factorization model in collaborative filtering algorithm: A survey,” arXiv preprint https://arxiv.org/abs/1503.07475, 2015. http://dx.doi.org/10.48550/arXiv.1503.07475
  20. P. Lops, M. de Gemmis, and G. Semeraro, “Content-based recommender systems: State of the art and trends,” in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Springer US, 2011, pp. 73–105.
  21. G. Adomavicius, Tuzhilin, and Alexander, “Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749, jun 2005. http://dx.doi.org/10.1109/TKDE.2005.99
  22. F. M. Harper and J. A. Konstan, “The movielens datasets: History and context,” ACM Trans. Interact. Intell. Syst., vol. 5, no. 4, Dec. 2015. http://dx.doi.org/10.1145/2827872
  23. L. Baltrunas, T. Makcinskas, and F. Ricci, “Group recommendations with rank aggregation and collaborative filtering,” in Proceedings of the fourth ACM conference on Recommender systems. Barcelona, Spain: ACM, 2010. http://dx.doi.org/10.1145/1864708.1864733 pp. 119–126.
  24. K. Järvelin and J. Kekäläinen, “Cumulated gain-based evaluation of ir techniques,” Transactions on Information Systems (TOIS), vol. 20, no. 4, pp. 422–446, 2002. http://dx.doi.org/10.1145/582415.582418
  25. C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval. New York, NY, USA: Cambridge University Press, 2008. ISBN 0521865719, 9780521865715
  26. T. Chai and R. R. Draxler, “Root mean square error (rmse) or mean absolute error (mae)? – arguments against avoiding rmse in the literature,” Geoscientific Model Development, vol. 7, no. 3, pp. 1247–1250, 2014. http://dx.doi.org/10.5194/gmd-7-1247-2014
  27. E. J. Gilroy, R. M. Hirsch, and T. A. Cohn, “Mean square error of regression-based constituent transport estimates,” Water Resources Research, vol. 26, no. 9, pp. 2069–2077, 1990. http://dx.doi.org/10.1029/WR026i009p02069