Neural Collaborative Filtering Bandits via Meta Learning. (arXiv:2201.13395v2 [cs.LG] UPDATED)

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 …

arxiv collaborative collaborative filtering learning meta