  Most sequential recommendation models capture the features of consecutive
items in a user-item interaction history. Though effective, their
representation expressiveness is still hindered by the sparse learning signals.
As a result, the sequential recommender is prone to make inconsistent
predictions. In this paper, we propose a model, SSI, to improve sequential
recommendation consistency with Self-Supervised Imitation. Precisely, we
extract the consistency knowledge by utilizing three self-supervised
pre-training tasks, where temporal consistency and persona consistency capture
user-interaction dynamics in terms of the chronological order and persona
sensitivities, respectively. Furthermore, to provide the model with a global
perspective, global session consistency is introduced by maximizing the mutual
information among global and local interaction sequences. Finally, to
comprehensively take advantage of all three independent aspects of
consistency-enhanced knowledge, we establish an integrated imitation learning
framework. The consistency knowledge is effectively internalized and
transferred to the student model by imitating the conventional prediction logit
as well as the consistency-enhanced item representations. In addition, the
flexible self-supervised imitation framework can also benefit other student
recommenders. Experiments on four real-world datasets show that SSI effectively
outperforms the state-of-the-art sequential recommendation methods.
