  User cold-start recommendation is a long-standing challenge for recommender
systems due to the fact that only a few interactions of cold-start users can be
exploited. Recent studies seek to address this challenge from the perspective
of meta learning, and most of them follow a manner of parameter initialization,
where the model parameters can be learned by a few steps of gradient updates.
While these gradient-based meta-learning models achieve promising performances
to some extent, a fundamental problem of them is how to adapt the global
knowledge learned from previous tasks for the recommendations of cold-start
users more effectively. In this paper, we develop a novel meta-learning
recommender called task-adaptive neural process (TaNP). TaNP is a new member of
the neural process family, where making recommendations for each user is
associated with a corresponding stochastic process. TaNP directly maps the
observed interactions of each user to a predictive distribution, sidestepping
some training issues in gradient-based meta-learning models. More importantly,
to balance the trade-off between model capacity and adaptation reliability, we
introduce a novel task-adaptive mechanism. It enables our model to learn the
relevance of different tasks and customize the global knowledge to the
task-related decoder parameters for estimating user preferences. We validate
TaNP on multiple benchmark datasets in different experimental settings.
Empirical results demonstrate that TaNP yields consistent improvements over
several state-of-the-art meta-learning recommenders.
