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.

中文翻译：

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通过领域偏好建模进行多领域推荐吸引用户

最近，网络平台已经同时运营各种服务领域。针对运营多个服务域的平台，我们引入了一项新任务，即吸引用户的多域推荐（MDRAU），该任务通过使用来自每个用户尚未交互的多个“不可见”域的知识来推荐项目用户的“看到”域。在本文中，我们指出了 MDRAU 任务的两个挑战。首先，从可见域到不可见域的映射有多种可能的组合，因为用户通常与服务域的不同子集进行交互。其次，用户可能对每个目标未见域有不同的偏好，这要求推荐反映用户对域和项目的偏好。为了应对这些挑战，我们提出了 DRIP 框架，该框架在两个级别（即领域和项目）对用户的偏好进行建模，并通过屏蔽领域建模以统一的方式学习各种可见与不可见的领域映射。我们广泛的实验证明了 DRIP 在 MDRAU 任务中的有效性及其捕获用户域级偏好的能力。