Abstract
Group recommendation constitutes a burgeoning research focus in recommendation systems. Despite a multitude of approaches achieving satisfactory outcomes, they still fail to address two major challenges: 1) these methods confine themselves to capturing user preferences exclusively within groups, neglecting to consider user collaborative signals beyond groups, which reveal users’ potential interests; 2) they do not sufficiently take into account the impact of multiple factors on group decision-making, such as individual expertise and influence, and the group’s general preferences. To tackle these challenges, we propose a new model named DDGR (Dual-Graph Convolutional Network and Dual-View Fusion for Group Recommendation), designed to capture representations addressing two aspects: member preferences and group preferences. DDGR consists of two components: 1) a dual-graph convolutional network that combines the benefits of both hypergraphs and graphs to fully explore member potential interests and collaborative signals; 2) a dual-view fusion strategy that accurately simulates the group negotiation process to model the impact of multiple factors from member and group view, which can obtain semantically rich group representations. Thorough validation on two real-world datasets indicates that our model significantly surpasses state-of-the-art methods.
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
References
Cao, D., He, X., Miao, L., An, Y., Yang, C., Hong, R.: Attentive group recommendation. In: The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 645–654 (2018)
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)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-N recommendation tasks. In: ACM Conference on Recommender Systems, pp. 39–46 (2010)
Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3558–3565 (2019)
Guo, L., Yin, H., Chen, T., Zhang, X., Zheng, K.: Hierarchical hyperedge embedding-based representation learning for group recommendation. ACM Trans. Inf. Syst. 40(1), 1–27 (2021)
Guo, L., Yin, H., Wang, Q., Cui, B., Huang, Z., Cui, L.: Group recommendation with latent voting mechanism. In: IEEE International Conference on Data Engineering, pp. 121–132. IEEE (2020)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: International Conference on World Wide Web, pp. 173–182 (2017)
He, Z., Chow, C.Y., Zhang, J.D.: GAME: learning graphical and attentive multi-view embeddings for occasional group recommendation. In: International ACM SIGIR Conference on Research and Development in Information Retrieval (2020)
Jia, R., Zhou, X., Dong, L., Pan, S.: Hypergraph convolutional network for group recommendation. In: IEEE International Conference on Data Mining, pp. 260–269. IEEE (2021)
Liu, X., Tian, Y., Ye, M., Lee, W.C.: Exploring personal impact for group recommendation. In: ACM International Conference on Information and Knowledge Management, pp. 674–683 (2012)
Wang, X., He, X., Nie, L., Chua, T.S.: Item silk road: recommending items from information domains to social users. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 185–194 (2017)
Wu, X., et al.: ConsRec: learning consensus behind interactions for group recommendation. In: Proceedings of the ACM Web Conference 2023, pp. 240–250 (2023)
Xia, X., Yin, H., Yu, J., Wang, Q., Cui, L., Zhang, X.: Self-supervised hypergraph convolutional networks for session-based recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4503–4511 (2021)
Yin, H., Wang, Q., Zheng, K., Li, Z., Yang, J., Zhou, X.: Social influence-based group representation learning for group recommendation. In: International Conference on Data Engineering, pp. 566–577. IEEE (2019)
Yin, H., Wang, Q., Zheng, K., Li, Z., Zhou, X.: Overcoming data sparsity in group recommendation. IEEE Trans. Knowl. Data Eng. 34, 3447–3460 (2020)
Yuan, Q., Cong, G., Lin, C.Y.: COM: a generative model for group recommendation. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 163–172 (2014)
Zhang, J., Gao, M., Yu, J., Guo, L., Li, J., Yin, H.: Double-scale self-supervised hypergraph learning for group recommendation. In: ACM International Conference on Information and Knowledge Management, pp. 2557–2567 (2021)
Acknowledgments
This work was supported by National Natural Science Foundation of China (No.62272290, 62172088) and Shanghai Natural Science Foundation(No.21ZR1400400).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhou, C., Zou, G., Hu, S., Lv, H., Wu, L., Zhang, B. (2024). Dual-Graph Convolutional Network and Dual-View Fusion for Group Recommendation. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_19
Download citation
DOI: https://doi.org/10.1007/978-981-97-2262-4_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-2264-8
Online ISBN: 978-981-97-2262-4
eBook Packages: Computer ScienceComputer Science (R0)