  Many previous studies aim to augment collaborative filtering with deep neural
network techniques, so as to achieve better recommendation performance.
However, most existing deep learning-based recommender systems are designed for
modeling singular type of user-item interaction behavior, which can hardly
distill the heterogeneous relations between user and item. In practical
recommendation scenarios, there exist multityped user behaviors, such as browse
and purchase. Due to the overlook of user's multi-behavioral patterns over
different items, existing recommendation methods are insufficient to capture
heterogeneous collaborative signals from user multi-behavior data. Inspired by
the strength of graph neural networks for structured data modeling, this work
proposes a Graph Neural Multi-Behavior Enhanced Recommendation (GNMR) framework
which explicitly models the dependencies between different types of user-item
interactions under a graph-based message passing architecture. GNMR devises a
relation aggregation network to model interaction heterogeneity, and
recursively performs embedding propagation between neighboring nodes over the
user-item interaction graph. Experiments on real-world recommendation datasets
show that our GNMR consistently outperforms state-of-the-art methods. The
source code is available at https://github.com/akaxlh/GNMR.
