Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users' potential interactions on the target behavior (e.g., buy), is regarded as an effective way to alleviate the data sparsity or cold-start issues in recommendation. Multi-behaviors are often taken in certain orders in real-world applications (e.g., click>cart>buy). In a behavior chain, a latter behavior usually exhibits a stronger signal of user preference than the former one does. Most existing multi-behavior models fail to capture such dependencies in a behavior chain for embedding learning. In this work, we propose a novel multi-behavior recommendation model with cascading graph convolution networks (named MB-CGCN). In MB-CGCN, the embeddings learned from one behavior are used as the input features for the next behavior's embedding learning after a feature transformation operation. In this way, our model explicitly utilizes the behavior dependencies in embedding learning. Experiments on two benchmark datasets demonstrate the effectiveness of our model on exploiting multi-behavior data. It outperforms the best baseline by 33.7% and 35.9% on average over the two datasets in terms of Recall@10 and NDCG@10, respectively.

翻译：多行为推荐通过利用辅助行为（如点击和加购）来帮助预测用户在目标行为（如购买）上的潜在交互，被认为是缓解推荐系统中数据稀疏或冷启动问题的有效方法。在现实应用中，多行为通常按特定顺序发生（例如点击>加购>购买）。在行为链中，后一行为通常比前一行为表现出更强的用户偏好信号。现有的大多数多行为模型未能捕捉行为链中的这种依赖关系以进行嵌入学习。本文提出了一种新颖的基于级联图卷积网络的多行为推荐模型（简称MB-CGCN）。在MB-CGCN中，从一个行为学习到的嵌入经过特征变换操作后，被用作下一个行为嵌入学习的输入特征。通过这种方式，我们的模型显式地利用了嵌入学习中的行为依赖关系。在两个基准数据集上的实验表明，该模型在利用多行为数据方面具有有效性。在两个数据集上，它在Recall@10和NDCG@10指标上平均分别比最优基线高出33.7%和35.9%。