  Cold-start issues have been more and more challenging for providing accurate
recommendations with the fast increase of users and items. Most existing
approaches attempt to solve the intractable problems via content-aware
recommendations based on auxiliary information and/or cross-domain
recommendations with transfer learning. Their performances are often
constrained by the extremely sparse user-item interactions, unavailable side
information, or very limited domain-shared users. Recently, meta-learners with
meta-augmentation by adding noises to labels have been proven to be effective
to avoid overfitting and shown good performance on new tasks. Motivated by the
idea of meta-augmentation, in this paper, by treating a user's preference over
items as a task, we propose a so-called Diverse Preference Augmentation
framework with multiple source domains based on meta-learning (referred to as
MetaDPA) to i) generate diverse ratings in a new domain of interest (known as
target domain) to handle overfitting on the case of sparse interactions, and to
ii) learn a preference model in the target domain via a meta-learning scheme to
alleviate cold-start issues. Specifically, we first conduct multi-source domain
adaptation by dual conditional variational autoencoders and impose a
Multi-domain InfoMax (MDI) constraint on the latent representations to learn
domain-shared and domain-specific preference properties. To avoid overfitting,
we add a Mutually-Exclusive (ME) constraint on the output of decoders to
generate diverse ratings given content data. Finally, these generated diverse
ratings and the original ratings are introduced into the meta-training
procedure to learn a preference meta-learner, which produces good
generalization ability on cold-start recommendation tasks. Experiments on
real-world datasets show our proposed MetaDPA clearly outperforms the current
state-of-the-art baselines.
