In the industrial e-commerce recommender systems, the sparsity of user–item interaction limits the improvement of the performance of collaborative filtering recommendation. Some studies have leveraged attribute co-occurrence or similar neighbors to enhance the semantic representation quality of users and items. Previous methods consider collaborative signals of homogeneous type nodes, such as $<user,user>\to user$ and $<item,item>\to item$. By exploiting homogeneous and heterogeneous signals of attribute and neighbor views, we design a multiview graph collaborative filtering (MVGCF) network for recommendation. The MVGCF model utilizes both co-occurrence features of various attribute values and collaborative preference of various neighbors to learn the embedding representation of nodes. Experimental results show that the MVGCF is superior to the state-of-the-art models in AUC and logloss metrics by 1.41% and 3.12% for MovieLens 1M dataset, and by 2.35% and 2.31% for BookCrossing dataset. Aiming at the sparse problem with a small amount of interaction records, our findings is that attribute co-occurrence and neighbor collaboration can improve the accuracy and provide a good explanation for e-commerce recommender systems.

在工业电子商务推荐系统中，用户-项目交互的稀疏性限制了协同过滤推荐性能的提高。一些研究利用属性共现或相似邻居来增强用户和项目的语义质量表示。以前的方法考虑同质类型节点的协作信号，例如$<\mathrm{或者}s和r,\mathrm{或者}s和r>\to \mathrm{或者}s和r$和$<哟你和米,哟你和米>\to 哟你和米$. 通过利用属性和邻居视图的同质和异构信号，我们设计了一个多视图图协同过滤（MVGCF）网络进行推荐。MVGCF模型同时利用各种属性值的共现特征和各种邻居的协同偏好来学习节点的嵌入表示。实验结果表明，对于 MovieLens 1M 数据集，MVGCF 在 AUC 和 logloss 指标上优于最先进的模型 1.41% 和 3.12%，在 BookCrossing 数据集上优于 2.35% 和 2.31%。针对交互记录较少的稀疏问题，我们的发现是属性共现和邻居协作可以提高准确性，并为电子商务推荐系统提供了很好的解释。