  Multi-Target Cross Domain Recommendation(CDR) has attracted a surge of
interest recently, which intends to improve the recommendation performance in
multiple domains (or systems) simultaneously. Most existing multi-target CDR
frameworks primarily rely on the existence of the majority of overlapped users
across domains. However, general practical CDR scenarios cannot meet the
strictly overlapping requirements and only share a small margin of common users
across domains}. Additionally, the majority of users have quite a few
historical behaviors in such small-overlapping CDR scenarios}. To tackle the
aforementioned issues, we propose a simple-yet-effective neural node matching
based framework for more general CDR settings, i.e., only (few) partially
overlapped users exist across domains and most overlapped as well as
non-overlapped users do have sparse interactions. The present framework} mainly
contains two modules: (i) intra-to-inter node matching module, and (ii) intra
node complementing module. Concretely, the first module conducts
intra-knowledge fusion within each domain and subsequent inter-knowledge fusion
across domains by fully connected user-user homogeneous graph information
aggregating.
