TY - GEN
T1 - SuGeR
T2 - 31st ACM International Conference on Information and Knowledge Management, CIKM 2022
AU - Zhang, Zhenning
AU - Du, Boxin
AU - Tong, Hanghang
N1 - This work is supported by NSF (1947135,and 2134079), the NSF Program on Fairness in AI in collaboration with Amazon (1939725), DARPA (HR001121C0165),NIFA (2020-67021-32799), and ARO (W911NF2110088). The content of the information in this document does not necessarily reflect the position or the policy of the Government or Amazon, and no official endorsement should be inferred. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.
PY - 2022/10/17
Y1 - 2022/10/17
N2 - Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users. Although Graph Neural Networks (GNNs) have been applied to this problem and achieved superior performance, existing methods underexplore the graph-level GNN methods, which exhibit great potential in traditional recommender system. Furthermore, they usually lack the transferability from one domain with sufficient supervision to another domain which might suffer from the label scarcity issue. In this work, we propose a subgraph-based Graph Neural Network model, SuGeR, for bundle recommendation to handle these limitations. SuGeR generates heterogeneous subgraphs around the user-bundle pairs and then maps those subgraphs to the users' preference predictions via neural relational graph propagation. Experimental results show that SUGER significantly outperforms the state-of-the-art baselines in the basic and the transfer bundle recommendation tasks by up to 77.17% by NDCG@40. The source code is available at: https://github.com/Zhang-Zhenning/SUGER.
AB - Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users. Although Graph Neural Networks (GNNs) have been applied to this problem and achieved superior performance, existing methods underexplore the graph-level GNN methods, which exhibit great potential in traditional recommender system. Furthermore, they usually lack the transferability from one domain with sufficient supervision to another domain which might suffer from the label scarcity issue. In this work, we propose a subgraph-based Graph Neural Network model, SuGeR, for bundle recommendation to handle these limitations. SuGeR generates heterogeneous subgraphs around the user-bundle pairs and then maps those subgraphs to the users' preference predictions via neural relational graph propagation. Experimental results show that SUGER significantly outperforms the state-of-the-art baselines in the basic and the transfer bundle recommendation tasks by up to 77.17% by NDCG@40. The source code is available at: https://github.com/Zhang-Zhenning/SUGER.
KW - graph neural networks
KW - recommender system
UR - http://www.scopus.com/inward/record.url?scp=85140827573&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85140827573&partnerID=8YFLogxK
U2 - 10.1145/3511808.3557707
DO - 10.1145/3511808.3557707
M3 - Conference contribution
AN - SCOPUS:85140827573
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 4712
EP - 4716
BT - CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 17 October 2022 through 21 October 2022
ER -