Unified Group Recommendation Towards Multiple Criteria

  • Yi Wu
  • Ning YangEmail author
  • Huanrui Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11642)


In online social networks, a growing number of people are willing to share their activities with ones who have common interests. This motivates the research on group recommendation, which focuses on the issue of recommending items to a group of users. The existing methods on addressing the problem of grouping users and making recommendations for the formed groups simultaneously, however, often suffer from two defects. The first one is that they separate group partition and group recommendation, which often reduce the overall group satisfaction. The second one is that they tend to pursue a single objective optimum instead of making a balance between multiple objectives.

In this paper, we strive to tackle the key problem of grouping users and making recommendations for the formed groups simultaneously. It is a challenging problem due to the differences between user preferences over items, and how to make a trade-off among their preferences for the recommended items is still the main research point. To address these challenges, we present a Unified Group Recommendation (UGR) model, which intertwines the user grouping and group recommendation in a unified multi-objective optimization process that makes a balance between multiple criteria, including maximizing overall group satisfaction, social relationship density, and overall group fairness. Extensive experiments on two real-world datasets verify the effectiveness of our method.


Group partition Group recommendation Multi-objective optimization 


  1. 1.
    Amer-Yahia, S., Roy, S.B., Chawlat, A., Das, G., Yu, C.: Group recommendation: semantics and efficiency. Proc. VLDB Endow. 2(1), 754–765 (2009)CrossRefGoogle Scholar
  2. 2.
    Basu Roy, S., Lakshmanan, L.V., Liu, R.: From group recommendations to group formation. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1603–1616. ACM (2015)Google Scholar
  3. 3.
    Beckmann, C., Gross, T.: Towards a group recommender process model for ad-hoc groups and on-demand recommendations. In: Proceedings of the 16th ACM International Conference on Supporting Group Work, pp. 329–330. ACM (2010)Google Scholar
  4. 4.
    Boratto, L., Carta, S.: The rating prediction task in a group recommender system that automatically detects groups: architectures, algorithms, and performance evaluation. J. Intell. Inf. Syst. 45(2), 221–245 (2015)CrossRefGoogle Scholar
  5. 5.
    Boratto, L., Carta, S., Fenu, G.: Investigating the role of the rating prediction task in granularity-based group recommender systems and big data scenarios. Inf. Sci. 378, 424–443 (2017)CrossRefGoogle Scholar
  6. 6.
    Carvalho, L.A.M.C., Macedo, H.T.: Users’ satisfaction in recommendation systems for groups: an approach based on noncooperative games. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 951–958. ACM (2013)Google Scholar
  7. 7.
    Chen, C., Zheng, X., Wang, Y., Hong, F., Lin, Z., et al.: Context-aware collaborative topic regression with social matrix factorization for recommender systems. In AAAI, pp. 9–15 (2014)Google Scholar
  8. 8.
    De Pessemier, T., Dooms, S., Martens, L.: Comparison of group recommendation algorithms. Multimedia Tools Appl. 72(3), 2497–2541 (2014)CrossRefGoogle Scholar
  9. 9.
    Deb, K.: Multi-objective optimization. In: Burke, E., Kendall, G. (eds.) Search Methodologies, pp. 403–449. Springer, Boston (2014). Scholar
  10. 10.
    Deb, K., Sindhya, K., Hakanen, J.: Multi-objective optimization. In: Decision Sciences: Theory and Practice (2016)CrossRefGoogle Scholar
  11. 11.
    Fang, G., Su, L., Jiang, D., Wu, L.: Group recommendation systems based on external social-trust networks. In: Wireless Communications and Mobile Computing (2018)Google Scholar
  12. 12.
    Guo, C., Li, B., Tian, X.: Flickr group recommendation using rich social media information. Neurocomputing 204, 8–16 (2016)CrossRefGoogle Scholar
  13. 13.
    Guo, Z., Tang, C., Tang, H., Fu, Y., Niu, W.: A novel group recommendation mechanism from the perspective of preference distribution. IEEE Access 6, 5865–5878 (2018)CrossRefGoogle Scholar
  14. 14.
    Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: Grouplens: applying collaborative filtering to usenet news. Commun. ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar
  15. 15.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)CrossRefGoogle Scholar
  16. 16.
    Li, H., Liu, Y., Qian, Y., Mamoulis, N., Tu, W., Cheung, D.W.: HHMF: hidden hierarchical matrix factorization for recommender systems. In: Data Mining and Knowledge Discovery (2019)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Linden, G., Smith, B., York, J.: recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 1, 76–80 (2003)CrossRefGoogle Scholar
  18. 18.
    Mahyar, H., Ghalebi K, E., Morshedi, S.M., Khalili, S., Grosu, R., Movaghar, A.: Centrality-based group formation in group recommender systems. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 1187–1196. International World Wide Web Conferences Steering Committee (2017)Google Scholar
  19. 19.
    Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, pp. 1257–1264 (2008)Google Scholar
  20. 20.
    Özsoy, M.G., Polat, F., Alhajj, R.: Multi-objective optimization based location and social network aware recommendation. In: 2014 International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), pp. 233–242. IEEE (2014)Google Scholar
  21. 21.
    Polat, H., Du, W.: SVD-based collaborative filtering with privacy. In: Proceedings of the 2005 ACM Symposium on Applied Computing, pp. 791–795. ACM (2005)Google Scholar
  22. 22.
    Qi, S., Mamoulis, N., Pitoura, E., Tsaparas, P.: Recommending packages to groups. In: 2016 IEEE 16th International Conference on Data Mining (ICDM), pp. 449–458. IEEE (2016)Google Scholar
  23. 23.
    Qin, D., Zhou, X., Chen, L., Huang, G., Zhang, Y.: Dynamic connection-based social group recommendation. IEEE Trans. Knowl. Data Eng. PP, 1 (2018)CrossRefGoogle Scholar
  24. 24.
    Ribeiro, M.T., Lacerda, A., Veloso, A., Ziviani, N.: Pareto-efficient hybridization for multi-objective recommender systems. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 19–26. ACM (2012)Google Scholar
  25. 25.
    Rodriguez, M., Posse, C., Zhang, E.: Multiple objective optimization in recommender systems. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 11–18. ACM (2012)Google Scholar
  26. 26.
    Salehi-Abari, A., Boutilier, C.: Preference-oriented social networks: group recommendation and inference. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 35–42. ACM (2015)Google Scholar
  27. 27.
    Serbos, D., Qi, S., Mamoulis, N., Pitoura, E., Tsaparas, P.: Fairness in package-to-group recommendations. In: Proceedings of the 26th International Conference on World Wide Web, pp. 371–379. International World Wide Web Conferences Steering Committee (2017)Google Scholar
  28. 28.
    Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. In: Advances in Artificial Intelligence (2009)CrossRefGoogle Scholar
  29. 29.
    Wang, S., Gong, M., Li, H., Yang, J.: Multi-objective optimization for long tail recommendation. Knowl.-Based Syst. 104, 145–155 (2016)CrossRefGoogle Scholar
  30. 30.
    Lin, X., Zhang, M., Zhang, Y., Gu, Z., Liu, Y., Ma, S.: Fairness-aware group recommendation with pareto-efficiency. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 107–115. ACM (2017)Google Scholar
  31. 31.
    Zeng, X., Wu, B., Shi, J., Liu, C., Guo, Q.: Parallelization of latent group model for group recommendation algorithm. In: IEEE International Conference on Data Science in Cyberspace (DSC), pp. 80–89. IEEE (2016)Google Scholar
  32. 32.
    Zhao, J., Liu, K., Tang, F.: A group recommendation strategy based on user’s interaction behavior. In: 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 1170–1174. IEEE (2017)Google Scholar
  33. 33.
    Zhu, Q., Wang, S., Cheng, B., Sun, Q., Yang, F., Chang, R.N.: Context-aware group recommendation for point-of-interests. IEEE Access 6, 12129–12144 (2018)CrossRefGoogle Scholar
  34. 34.
    Zuo, Y., Gong, M., Zeng, J., Ma, L., Jiao, L.: Personalized recommendation based on evolutionary multi-objective optimization [research frontier]. IEEE Comput. Intell. Mag. 10(1), 52–62 (2015)CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.College of Computer ScienceSichuan UniversityChengduChina

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