  User purchasing prediction with multi-behavior information remains a
challenging problem for current recommendation systems. Various methods have
been proposed to address it via leveraging the advantages of graph neural
networks (GNNs) or multi-task learning. However, most existing works do not
take the complex dependencies among different behaviors of users into
consideration. They utilize simple and fixed schemes, like neighborhood
information aggregation or mathematical calculation of vectors, to fuse the
embeddings of different user behaviors to obtain a unified embedding to
represent a user's behavioral patterns which will be used in downstream
recommendation tasks. To tackle the challenge, in this paper, we first propose
the concept of hyper meta-path to construct hyper meta-paths or hyper
meta-graphs to explicitly illustrate the dependencies among different behaviors
of a user. How to obtain a unified embedding for a user from hyper meta-paths
and avoid the previously mentioned limitations simultaneously is critical.
Thanks to the recent success of graph contrastive learning, we leverage it to
learn embeddings of user behavior patterns adaptively instead of assigning a
fixed scheme to understand the dependencies among different behaviors. A new
graph contrastive learning based framework is proposed by coupling with hyper
meta-paths, namely HMG-CR, which consistently and significantly outperforms all
baselines in extensive comparison experiments.
