论文标题
suger：用于捆绑建议的基于子图的图形卷积网络方法
SUGER: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation
论文作者
论文摘要
Bundle建议是推荐系统中新兴的研究方向，重点是为用户推荐定制的物品捆绑包。尽管已在此问题中应用了图神经网络（GNN）并实现卓越的性能，但现有的方法underxplore graph televel gnn方法在传统推荐系统中具有巨大的潜力。此外，他们通常缺乏从一个领域的可转移性，并有足够的监督到另一个可能遭受标签稀缺问题的领域。在这项工作中，我们提出了一个基于子图的图形神经网络模型SUGER，以捆绑建议来应对这些限制。 Suger周围生成了用户捆绑对的异构子图，然后通过神经关系图传播将这些子图映射到用户的首选项预测。实验结果表明，Suger在基本和转移束建议问题中都显着胜过最先进的基线。
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 in this problem and achieve 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 both the basic and the transfer bundle recommendation problems.
