  Contextual multi-armed bandits provide powerful tools to solve the
exploitation-exploration dilemma in decision making, with direct applications
in the personalized recommendation. In fact, collaborative effects among users
carry the significant potential to improve the recommendation. In this paper,
we introduce and study the problem by exploring `Neural Collaborative Filtering
Bandits', where the rewards can be non-linear functions and groups are formed
dynamically given different specific contents. To solve this problem, inspired
by meta-learning, we propose Meta-Ban (meta-bandits), where a meta-learner is
designed to represent and rapidly adapt to dynamic groups, along with a
UCB-based exploration strategy. Furthermore, we analyze that Meta-Ban can
achieve the regret bound of $\mathcal{O}(\sqrt{T \log T})$, improving a
multiplicative factor $\sqrt{\log T}$ over state-of-the-art related works. In
the end, we conduct extensive experiments showing that Meta-Ban significantly
outperforms six strong baselines.
