  This paper explores meta-learning in sequential recommendation to alleviate
the item cold-start problem. Sequential recommendation aims to capture user's
dynamic preferences based on historical behavior sequences and acts as a key
component of most online recommendation scenarios. However, most previous
methods have trouble recommending cold-start items, which are prevalent in
those scenarios. As there is generally no side information in the setting of
sequential recommendation task, previous cold-start methods could not be
applied when only user-item interactions are available. Thus, we propose a
Meta-learning-based Cold-Start Sequential Recommendation Framework, namely
Mecos, to mitigate the item cold-start problem in sequential recommendation.
This task is non-trivial as it targets at an important problem in a novel and
challenging context. Mecos effectively extracts user preference from limited
interactions and learns to match the target cold-start item with the potential
user. Besides, our framework can be painlessly integrated with neural
network-based models. Extensive experiments conducted on three real-world
datasets verify the superiority of Mecos, with the average improvement up to
99%, 91%, and 70% in HR@10 over state-of-the-art baseline methods.
