  Cold-start problems are long-standing challenges for practical
recommendations. Most existing recommendation algorithms rely on extensive
observed data and are brittle to recommendation scenarios with few
interactions. This paper addresses such problems using few-shot learning and
meta learning. Our approach is based on the insight that having a good
generalization from a few examples relies on both a generic model
initialization and an effective strategy for adapting this model to newly
arising tasks. To accomplish this, we combine the scenario-specific learning
with a model-agnostic sequential meta-learning and unify them into an
integrated end-to-end framework, namely Scenario-specific Sequential Meta
learner (or s^2 meta). By doing so, our meta-learner produces a generic initial
model through aggregating contextual information from a variety of prediction
tasks while effectively adapting to specific tasks by leveraging
learning-to-learn knowledge. Extensive experiments on various real-world
datasets demonstrate that our proposed model can achieve significant gains over
the state-of-the-arts for cold-start problems in online recommendation.
Deployment is at the Guess You Like session, the front page of the Mobile
Taobao.
