Learning accurate cross-domain preference mappings in the absence of overlapped users/items has presented a persistent challenge in Non-overlapping Cross-domain Recommendation (NOCDR). Despite the efforts made in previous studies to address NOCDR, several limitations still exist. Specifically, 1) while some approaches substitute overlapping users/items with overlapping behaviors, they cannot handle NOCDR scenarios where such auxiliary information is unavailable; 2) often, cross-domain preference mapping is modeled by learning deterministic explicit representation matchings between sampled users in two domains. However, this can be biased due to individual preferences and thus fails to incorporate preference continuity and universality of the general population. In light of this, we assume that despite the scattered nature of user behaviors, there exists a consistent latent preference distribution shared among common people. Modeling such distributions further allows us to capture the continuity in user behaviors within each domain and discover preference invariance across domains. To this end, we propose a Distributional domain-invariant Preference Matching method for non-overlapping Cross-Domain Recommendation (DPMCDR). For each domain, we hierarchically approximate a posterior of domain-level preference distribution with empirical evidence derived from user-item interactions. Next, we aim to build distributional implicit matchings between the domain-level preferences of two domains. This process involves mapping them to a shared latent space and seeking a consensus on domain-invariant preference by minimizing the distance between their distributional representations therein. In this way, we can identify the alignment of two non-overlapping domains if they exhibit similar patterns of domain-invariant preference.

翻译：在无重叠用户/物品的跨域推荐（NOCDR）中，学习准确的跨域偏好映射一直是持续挑战。尽管先前研究已尝试解决NOCDR问题，但仍存在若干局限：1）部分方法虽能以重叠行为替代重叠用户/物品，但无法处理此类辅助信息缺失的NOCDR场景；2）跨域偏好映射通常通过两个域中采样用户间的确定性显式表征匹配进行建模，然而这种匹配因个体偏好差异而存在偏差，未能融入群体偏好的连续性与普适性。基于此，我们假设尽管用户行为具有离散性，但人类群体中存在一致的潜在偏好分布共享。对此类分布的建模可捕捉各域内用户行为的连续性，并发现跨域偏好不变性。为此，我们提出面向无重叠跨域推荐的不变偏好分布匹配方法（DPMCDR）。在每域中，我们利用用户-物品交互的实证证据分层逼近域级偏好分布的后验，进而构建两域域级偏好间的分布隐含匹配。该过程通过将域级偏好映射至共享隐空间，并最小化其分布表征在该空间的距离以寻求域不变偏好的一致性。通过此方式，当两个无重叠域呈现相似域不变偏好模式时，即可识别其对齐关系。