  Cross-domain recommendation (CDR) has been proven as a promising way to
tackle the user cold-start problem, which aims to make recommendations for
users in the target domain by transferring the user preference derived from the
source domain. Traditional CDR studies follow the embedding and mapping (EMCDR)
paradigm, which transfers user representations from the source to target domain
by learning a user-shared mapping function, neglecting the user-specific
preference. Recent CDR studies attempt to learn user-specific mapping functions
in meta-learning paradigm, which regards each user's CDR as an individual task,
but neglects the preference correlations among users, limiting the beneficial
information for user representations. Moreover, both of the paradigms neglect
the explicit user-item interactions from both domains during the mapping
process. To address the above issues, this paper proposes a novel CDR framework
with neural process (NP), termed as CDRNP. Particularly, it develops the
meta-learning paradigm to leverage user-specific preference, and further
introduces a stochastic process by NP to capture the preference correlations
among the overlapping and cold-start users, thus generating more powerful
mapping functions by mapping the user-specific preference and common preference
correlations to a predictive probability distribution. In addition, we also
introduce a preference remainer to enhance the common preference from the
overlapping users, and finally devises an adaptive conditional decoder with
preference modulation to make prediction for cold-start users with items in the
target domain. Experimental results demonstrate that CDRNP outperforms previous
SOTA methods in three real-world CDR scenarios.
