  Sequential Recommendation (SR) characterizes evolving patterns of user
behaviors by modeling how users transit among items. However, the short
interaction sequences limit the performance of existing SR. To solve this
problem, we focus on Cross-Domain Sequential Recommendation (CDSR) in this
paper, which aims to leverage information from other domains to improve the
sequential recommendation performance of a single domain. Solving CDSR is
challenging. On the one hand, how to retain single domain preferences as well
as integrate cross-domain influence remains an essential problem. On the other
hand, the data sparsity problem cannot be totally solved by simply utilizing
knowledge from other domains, due to the limited length of the merged
sequences. To address the challenges, we propose DDGHM, a novel framework for
the CDSR problem, which includes two main modules, i.e., dual dynamic graph
modeling and hybrid metric training. The former captures intra-domain and
inter-domain sequential transitions through dynamically constructing two-level
graphs, i.e., the local graphs and the global graph, and incorporating them
with a fuse attentive gating mechanism. The latter enhances user and item
representations by employing hybrid metric learning, including collaborative
metric for achieving alignment and contrastive metric for preserving
uniformity, to further alleviate data sparsity issue and improve prediction
accuracy. We conduct experiments on two benchmark datasets and the results
demonstrate the effectiveness of DDHMG.
