  Online recommender systems should be always aligned with users' current
interest to accurately suggest items that each user would like. Since user
interest usually evolves over time, the update strategy should be flexible to
quickly catch users' current interest from continuously generated new user-item
interactions. Existing update strategies focus either on the importance of each
user-item interaction or the learning rate for each recommender parameter, but
such one-directional flexibility is insufficient to adapt to varying
relationships between interactions and parameters. In this paper, we propose
MeLON, a meta-learning based novel online recommender update strategy that
supports two-directional flexibility. It is featured with an adaptive learning
rate for each parameter-interaction pair for inducing a recommender to quickly
learn users' up-to-date interest. The procedure of MeLON is optimized following
a meta-learning approach: it learns how a recommender learns to generate the
optimal learning rates for future updates. Specifically, MeLON first enriches
the meaning of each interaction based on previous interactions and identifies
the role of each parameter for the interaction; and then combines these two
pieces of information to generate an adaptive learning rate. Theoretical
analysis and extensive evaluation on three real-world online recommender
datasets validate the effectiveness of MeLON.
