  Sequential recommendation aims to choose the most suitable items for a user
at a specific timestamp given historical behaviors. Existing methods usually
model the user behavior sequence based on the transition-based methods like
Markov Chain. However, these methods also implicitly assume that the users are
independent of each other without considering the influence between users. In
fact, this influence plays an important role in sequence recommendation since
the behavior of a user is easily affected by others. Therefore, it is desirable
to aggregate both user behaviors and the influence between users, which are
evolved temporally and involved in the heterogeneous graph of users and items.
In this paper, we incorporate dynamic user-item heterogeneous graphs to propose
a novel sequential recommendation framework. As a result, the historical
behaviors as well as the influence between users can be taken into
consideration. To achieve this, we firstly formalize sequential recommendation
as a problem to estimate conditional probability given temporal dynamic
heterogeneous graphs and user behavior sequences. After that, we exploit the
conditional random field to aggregate the heterogeneous graphs and user
behaviors for probability estimation, and employ the pseudo-likelihood approach
to derive a tractable objective function. Finally, we provide scalable and
flexible implementations of the proposed framework. Experimental results on
three real-world datasets not only demonstrate the effectiveness of our
proposed method but also provide some insightful discoveries on sequential
recommendation.
