  Adapters, a plug-in neural network module with some tunable parameters, have
emerged as a parameter-efficient transfer learning technique for adapting
pre-trained models to downstream tasks, especially for natural language
processing (NLP) and computer vision (CV) fields. Meanwhile, learning
recommendation models directly from raw item modality features -- e.g., texts
of NLP and images of CV -- can enable effective and transferable recommender
systems (called TransRec). In view of this, a natural question arises: can
adapter-based learning techniques achieve parameter-efficient TransRec with
good performance?
  To this end, we perform empirical studies to address several key
sub-questions. First, we ask whether the adapter-based TransRec performs
comparably to TransRec based on standard full-parameter fine-tuning? does it
hold for recommendation with different item modalities, e.g., textual RS and
visual RS. If yes, we benchmark these existing adapters, which have been shown
to be effective in NLP and CV tasks, in item recommendation tasks. Third, we
carefully study several key factors for the adapter-based TransRec in terms of
where and how to insert these adapters? Finally, we look at the effects of
adapter-based TransRec by either scaling up its source training data or scaling
down its target training data. Our paper provides key insights and practical
guidance on unified & transferable recommendation -- a less studied
recommendation scenario. We release our codes and other materials at:
https://github.com/westlake-repl/Adapter4Rec/.
