  Cold-start problems are enormous challenges in practical recommender systems.
One promising solution for this problem is cross-domain recommendation (CDR)
which leverages rich information from an auxiliary (source) domain to improve
the performance of recommender system in the target domain. In these CDR
approaches, the family of Embedding and Mapping methods for CDR (EMCDR) is very
effective, which explicitly learn a mapping function from source embeddings to
target embeddings with overlapping users. However, these approaches suffer from
one serious problem: the mapping function is only learned on limited
overlapping users, and the function would be biased to the limited overlapping
users, which leads to unsatisfying generalization ability and degrades the
performance on cold-start users in the target domain. With the advantage of
meta learning which has good generalization ability to novel tasks, we propose
a transfer-meta framework for CDR (TMCDR) which has a transfer stage and a meta
stage. In the transfer (pre-training) stage, a source model and a target model
are trained on source and target domains, respectively. In the meta stage, a
task-oriented meta network is learned to implicitly transform the user
embedding in the source domain to the target feature space. In addition, the
TMCDR is a general framework that can be applied upon various base models,
e.g., MF, BPR, CML. By utilizing data from Amazon and Douban, we conduct
extensive experiments on 6 cross-domain tasks to demonstrate the superior
performance and compatibility of TMCDR.
