Abstract:

Focusing on the problem that it is difficult for the existing group recommendation models based on Graph Neural Networks （GNNs） to fully utilize explicit and implicit interaction information， a Group Recommendation by GNN based on Multi-perspective learning （GRGM） model was proposed. Firstly， hypergraphs， bipartite graphs， as well as hypergraph projections were constructed according to the group interaction data， and the corresponding GNN was adopted aiming at the characteristics of each graph to extract node features of the graph， thereby fully expressing the explicit and implicit relationships among users， groups， and items. Then， a multi-perspective information fusion strategy was proposed to obtain the final group and item representations. Experimental results on Mafengwo， CAMRa2011， and Weeplases datasets show that compared to the baseline model ConsRec， GRGM model improves the Hit Ratio （HR@5， HR@1） and Normalized Discounted Cumulative Gain （NDCG@5， NDCG@10） by 3.38%， 1.96% and 3.67%， 3.84%， respectively， on Mafengwo dataset， 2.87%， 1.18% and 0.96%， 1.62%， respectively， on CAMRa2011 dataset， and 2.41%， 1.69% and 4.35%， and 2.60%， respectively， on Weeplaces dataset. It can be seen that GRGM model has better recommendation performance compared with the baseline models.

Key words: group recommendation, Graph Neural Network (GNN), multi-perspective learning, hypergraph, implicit information

摘要：

针对现有基于图神经网络（GNN）的群组推荐模型难以充分利用显隐式交互信息的问题，提出一种基于多视角学习的GNN群组推荐（GRGM）模型。先根据群组交互数据构造超图、二分图和超图投影图，并针对各个图结构的特性采用相应的GNN提取图节点特征，从而充分表达用户、群组和项目之间的显隐式关系；再提出一种多视角信息融合策略，以获取最终的群组和项目表示。在Mafengwo、CAMRa2011和Weeplaces数据集上的实验结果表明，相较于基线模型ConsRec，GRGM模型的命中率（HR@5、HR@10）和归一化折损累计增益（NDCG@5、NDCG@10）在Mafengwo数据集上分别提升了3.38%、1.96%和3.67%、3.84%，在CAMRa2011数据集上分别提升了2.87%、1.18%和0.96%、1.62%，在Weeplaces数据集上分别提升了2.41%、1.69%和4.35%、2.60%。可见，GRGM模型相较于对比模型具有更好的推荐性能。

关键词: 群组推荐, 图神经网络, 多视角学习, 超图, 隐式信息