  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.
