  A common challenge for most current recommender systems is the cold-start
problem. Due to the lack of user-item interactions, the fine-tuned recommender
systems are unable to handle situations with new users or new items. Recently,
some works introduce the meta-optimization idea into the recommendation
scenarios, i.e. predicting the user preference by only a few of past interacted
items. The core idea is learning a global sharing initialization parameter for
all users and then learning the local parameters for each user separately.
However, most meta-learning based recommendation approaches adopt
model-agnostic meta-learning for parameter initialization, where the global
sharing parameter may lead the model into local optima for some users. In this
paper, we design two memory matrices that can store task-specific memories and
feature-specific memories. Specifically, the feature-specific memories are used
to guide the model with personalized parameter initialization, while the
task-specific memories are used to guide the model fast predicting the user
preference. And we adopt a meta-optimization approach for optimizing the
proposed method. We test the model on two widely used recommendation datasets
and consider four cold-start situations. The experimental results show the
effectiveness of the proposed methods.
