  Recommender systems have been demonstrated to be effective to meet user's
personalized interests for many online services (e.g., E-commerce and online
advertising platforms). Recent years have witnessed the emerging success of
many deep learning-based recommendation models for augmenting collaborative
filtering architectures with various neural network architectures, such as
multi-layer perceptron and autoencoder. However, the majority of them model the
user-item relationship with single type of interaction, while overlooking the
diversity of user behaviors on interacting with items, which can be click,
add-to-cart, tag-as-favorite and purchase. Such various types of interaction
behaviors have great potential in providing rich information for understanding
the user preferences. In this paper, we pay special attention on user-item
relationships with the exploration of multi-typed user behaviors. Technically,
we contribute a new multi-behavior graph neural network (MBRec), which
specially accounts for diverse interaction patterns as well as the underlying
cross-type behavior inter-dependencies. In the MBRec framework, we develop a
graph-structured learning framework to perform expressive modeling of
high-order connectivity in behavior-aware user-item interaction graph. After
that, a mutual relation encoder is proposed to adaptively uncover complex
relational structures and make aggregations across layer-specific behavior
representations. Through comprehensive evaluation on real-world datasets, the
advantages of our MBRec method have been validated under different experimental
settings. Further analysis verifies the positive effects of incorporating the
multi-behavioral context into the recommendation paradigm. Additionally, the
conducted case studies offer insights into the interpretability of user
multi-behavior representations.
