  Recently, web platforms have been operating various service domains
simultaneously. Targeting a platform that operates multiple service domains, we
introduce a new task, Multi-Domain Recommendation to Attract Users (MDRAU),
which recommends items from multiple ``unseen'' domains with which each user
has not interacted yet, by using knowledge from the user's ``seen'' domains. In
this paper, we point out two challenges of MDRAU task. First, there are
numerous possible combinations of mappings from seen to unseen domains because
users have usually interacted with a different subset of service domains.
Second, a user might have different preferences for each of the target unseen
domains, which requires that recommendations reflect the user's preferences on
domains as well as items. To tackle these challenges, we propose DRIP framework
that models users' preferences at two levels (i.e., domain and item) and learns
various seen-unseen domain mappings in a unified way with masked domain
modeling. Our extensive experiments demonstrate the effectiveness of DRIP in
MDRAU task and its ability to capture users' domain-level preferences.
