Abstract
Group Recommendation (GR) is the task of recommending items for a group of users. Most of existing studies adopt heuristic or attention-based preference aggregation strategies to learn group preferences, which ignores the composition of the group and suffers seriously from the problem of group-item interactions sparsity. In this paper, we propose a new group recommendation model based on Dual-level Hypergraph Representation Learning (called DHRL), which well models the group decision-making process by considering user-item interactions, group-item interactions and group-group interactions. Specifically, we design a member-level hypergraph convolutional network to learn group members’ personal preferences from user-item interactions. We also design a group-level hypergraph convolutional network to capture group preferences with full consideration of both group-item interactions and group-group interactions. Finally, we propose a joint training strategy to ease data sparsity by combining the group recommendation task with the user recommendation task. The experiments demonstrate the effectiveness and the efficiency of our proposed method compared to several state-of-the-art methods in terms of HR and NDCG.
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Funding
This work was supported by the National Natural Science Foundation of China under Grant Nos. 62072084, 62172082 and 62072086, the Science Research Funds of Liaoning Province of China under Grant No. LJKZ0094, the Natural Science Foundation of Liaoning Province of China under Grant No. 2022-MS-171, the Science and Technology Program Major Project of Liaoning Province of China under Grant No. 2022JH1/10400009.
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Wu, D., Kou, Y., Shen, D., Nie, T., Li, D. (2022). Dual-level Hypergraph Representation Learning for Group Recommendation. In: Zhao, X., Yang, S., Wang, X., Li, J. (eds) Web Information Systems and Applications. WISA 2022. Lecture Notes in Computer Science, vol 13579. Springer, Cham. https://doi.org/10.1007/978-3-031-20309-1_48
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DOI: https://doi.org/10.1007/978-3-031-20309-1_48
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