  Cross-domain recommender (CDR) systems aim to enhance the performance of the
target domain by utilizing data from other related domains. However, irrelevant
information from the source domain may instead degrade target domain
performance, which is known as the negative transfer problem. There have been
some attempts to address this problem, mostly by designing adaptive
representations for overlapped users. Whereas, representation adaptions solely
rely on the expressive capacity of the CDR model, lacking explicit constraint
to filter the irrelevant source-domain collaborative information for the target
domain.
  In this paper, we propose a novel Collaborative information regularized User
Transformation (CUT) framework to tackle the negative transfer problem by
directly filtering users' collaborative information. In CUT, user similarity in
the target domain is adopted as a constraint for user transformation learning
to filter the user collaborative information from the source domain. CUT first
learns user similarity relationships from the target domain. Then,
source-target information transfer is guided by the user similarity, where we
design a user transformation layer to learn target-domain user representations
and a contrastive loss to supervise the user collaborative information
transferred. The results show significant performance improvement of CUT
compared with SOTA single and cross-domain methods. Further analysis of the
target-domain results illustrates that CUT can effectively alleviate the
negative transfer problem.
