  A fundamental challenge for sequential recommenders is to capture the
sequential patterns of users toward modeling how users transit among items. In
many practical scenarios, however, there are a great number of cold-start users
with only minimal logged interactions. As a result, existing sequential
recommendation models will lose their predictive power due to the difficulties
in learning sequential patterns over users with only limited interactions. In
this work, we aim to improve sequential recommendation for cold-start users
with a novel framework named MetaTL, which learns to model the transition
patterns of users through meta-learning. Specifically, the proposed MetaTL: (i)
formulates sequential recommendation for cold-start users as a few-shot
learning problem; (ii) extracts the dynamic transition patterns among users
with a translation-based architecture; and (iii) adopts meta transitional
learning to enable fast learning for cold-start users with only limited
interactions, leading to accurate inference of sequential interactions.
