  Collaborative filtering-based recommender systems that rely on a single type
of behavior often encounter serious sparsity issues in real-world applications,
leading to unsatisfactory performance. Multi-behavior Recommendation (MBR) is a
method that seeks to learn user preferences, represented as vector embeddings,
from auxiliary information. By leveraging these preferences for target behavior
recommendations, MBR addresses the sparsity problem and improves the accuracy
of recommendations. In this paper, we propose MB-HGCN, a novel multi-behavior
recommendation model that uses a hierarchical graph convolutional network to
learn user and item embeddings from coarse-grained on the global level to
fine-grained on the behavior-specific level. Our model learns global embeddings
from a unified homogeneous graph constructed by the interactions of all
behaviors, which are then used as initialized embeddings for behavior-specific
embedding learning in each behavior graph. We also emphasize the distinct of
the user and item behaviorspecific embeddings and design two
simple-yet-effective strategies to aggregate the behavior-specific embeddings
for users and items, respectively. Finally, we adopt multi-task learning for
optimization. Extensive experimental results on three real-world datasets
demonstrate that our model significantly outperforms the baselines, achieving a
relative improvement of 73.93% and 74.21% for HR@10 and NDCG@10, respectively,
on the Tmall datasets.
