Abstract
Group recommendation aims to suggest items that cater to the preferences of all members within the group. However, existing models often overlook the dynamic cognitive changes of group members, leading to inaccuracies. In this paper, we propose a novel Preference Learning framework based on Dynamic Dual-cognition for Group Recommendation (PL-DDGR), which aims to enhance group recommendation accuracy. Specifically, we first propose a preference learning approach based on graduality cognition, which can better understand and predict the subtle yet continuous shifts in member preferences. Then we propose a preference learning approach based on conformity cognition, which can capture the evolving nature of member conformity. We also propose a self-supervised multi-task joint training mechanism to optimize the learning of both graduality and conformity cognition simultaneously. The experiments demonstrate the effectiveness and superiority of our proposed framework.
This work was supported by the National Natural Science Foundation of China under Grant Nos. 62072084, 62472204 and 62172082, the Science and Technology Program Major Project of Liaoning Province of China under Grant No. 2022JH1/10400009, the Natural Science Foundation of Liaoning Province of China under Grant No. 2022-MS-171, and the Fundamental Research Funds for the Central Universities under Grant No. N2116008.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
He, C., Zhou, X., Wang, C., Cao, L., Shao, J., Tari, Z.: Influence-aware group recommendation for social media propagation. In: Proceedings of the 2024 IEEE International Conference on Data Mining (ICDM) (2024)
Qin, D., Zhou, X., Chen, L., Huang, G., Zhang, Y.: Dynamic connection-based social group recommendation. IEEE Trans. Knowl. Data Eng. 32(3), 453–467 (2020)
Baltrunas, L., Makcinskas, T., Ricci, F.: Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the 4th ACM Conference on Recommender Systems, pp. 119–126 (2010)
Read, M., Edwards, J.S., Gear, A.: Group preference aggregation rules: the results of a comparative analysis with in situ data. J. Oper. Res. Soc. 51(5), 557–563 (2000)
Boratto, L., Carta, S.: State-of-the-art in group recommendation and new approaches for automatic identification of groups. In: Information Retrieval and Mining in Distributed Environments, pp. 1–20. Springer (2011)
Seko, S., Yagi, T., Motegi, M., Muto, S.: Group recommendation using feature space representing behavioral tendency and power balance among members. In: Proceedings of the 5th ACM Conference on Recommender Systems, pp. 101–108 (2011)
Wang, W., Zhang, G., Lu, J.: Hierarchy visualization for group recommender systems. IEEE Trans. Syst. Man Cybern.: Syst. 49(6), 1152–1163 (2017)
Kim, H.-N., El Saddik, A.: A stochastic approach to group recommendations in social media systems. Inf. Syst. 50, 76–93 (2015)
Zhou, X., Liang, W., Huang, S., Fu, M.: Social recommendation with large-scale group decision-making for cyber-enabled online service. IEEE Trans. Comput. Soc. Syst. 6(5), 1073–1082 (2019)
Yin, H., Wang, Q., Zheng, K., Li, Z., Yang, J., Zhou, X.: Social influence-based group representation learning for group recommendation. In: Proceedings of the 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp. 566–577 (2019)
He, Z., Chow, C.-Y., Zhang, J.-D.: Game: learning graphical and attentive multi-view embeddings for occasional group recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 649–658 (2020)
Wang, W., et al.: Group-aware long-and short-term graph representation learning for sequential group recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1449–1458 (2020)
Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. 31(5), 833–852 (2018)
Jia, R., Zhou, X., Dong, L., Pan, S.: Hypergraph convolutional network for group recommendation. In: Proceedings of the 2021 IEEE International Conference on Data Mining (ICDM), pp. 260–269 (2021)
Liu, X., et al.: Self-supervised learning: generative or contrastive. IEEE Trans. Knowl. Data Eng. 35(1), 857–876 (2021)
Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. (CSUR) 52(1), 1–38 (2019)
Vinh Tran, L., Nguyen Pham, T.-A., Tay, Y., Liu, Y., Cong, G., Li, X.: Interact and decide: medley of sub-attention networks for effective group recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 255–264 (2019)
Hu, L., Cao, J., Xu, G., Cao, L., Gu, Z., Cao, W.: Deep modeling of group preferences for group-based recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28, no. 1 (2014)
Cao, D., He, X., Miao, L., An, Y., Yang, C., Hong, R.: Attentive group recommendation. In: Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 645–654 (2018)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017)
Zhang, J., Gao, M., Yu, J., Guo, L., Li, J., Yin, H.: Double-scale self-supervised hypergraph learning for group recommendation. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 2557–2567 (2021)
Wu, X., et al.: Consrec: learning consensus behind interactions for group recommendation. In: Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April–4 May 2023, pp. 240–250 (2023)
Zhang, K., Pelechrinis, K.: Understanding spatial homophily: the case of peer influence and social selection. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 271–282 (2014)
Cao, D., He, X., Miao, L., Xiao, G., Chen, H., Xu, J.: Social-enhanced attentive group recommendation. IEEE Trans. Knowl. Data Eng. 33(3), 1195–1209 (2019)
Guo, L., Yin, H., Chen, T., Zhang, X., Zheng, K.: Hierarchical hyperedge embedding-based representation learning for group recommendation. ACM Trans. Inf. Syst. (TOIS) 40(1), 1–27 (2021)
Sankar, A., Wu, Y., Wu, Y., Zhang, W., Yang, H., Sundaram, H.: GroupIM: a mutual information maximization framework for neural group recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1279–1288 (2020)
Chen, T., Yin, H., Long, J., Nguyen, Q.V.H., Wang, Y., Wang, M.: Thinking inside the box: learning hypercube representations for group recommendation. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1664–1673 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, C. et al. (2025). Preference Learning Based on Dynamic Dual-Cognition for Group Recommendation. In: Chen, T., Cao, Y., Nguyen, Q.V.H., Nguyen, T.T. (eds) Databases Theory and Applications. ADC 2024. Lecture Notes in Computer Science, vol 15449. Springer, Singapore. https://doi.org/10.1007/978-981-96-1242-0_19
Download citation
DOI: https://doi.org/10.1007/978-981-96-1242-0_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-96-1241-3
Online ISBN: 978-981-96-1242-0
eBook Packages: Computer ScienceComputer Science (R0)