  To address the long-standing data sparsity problem in recommender systems
(RSs), cross-domain recommendation (CDR) has been proposed to leverage the
relatively richer information from a richer domain to improve the
recommendation performance in a sparser domain. Although CDR has been
extensively studied in recent years, there is a lack of a systematic review of
the existing CDR approaches. To fill this gap, in this paper, we provide a
comprehensive review of existing CDR approaches, including challenges, research
progress, and future directions. Specifically, we first summarize existing CDR
approaches into four types, including single-target CDR, multi-domain
recommendation, dual-target CDR, and multi-target CDR. We then present the
definitions and challenges of these CDR approaches. Next, we propose a
full-view categorization and new taxonomies on these approaches and report
their research progress in detail. In the end, we share several promising
research directions in CDR.
