In addressing the persistent challenges of data-sparsity and cold-start issues in domain-expert recommender systems, Cross-Domain Recommendation (CDR) emerges as a promising methodology. CDR aims at enhancing prediction performance in the target domain by leveraging interaction knowledge from related source domains, particularly through users or items that span across multiple domains (e.g., Short-Video and Living-Room). For academic research purposes, there are a number of distinct aspects to guide CDR method designing, including the auxiliary domain number, domain-overlapped element, user-item interaction types, and downstream tasks. With so many different CDR combination scenario settings, the proposed scenario-expert approaches are tailored to address a specific vertical CDR scenario, and often lack the capacity to adapt to multiple horizontal scenarios. In an effect to coherently adapt to various scenarios, and drawing inspiration from the concept of domain-invariant transfer learning, we extend the former SOTA model UniCDR in five different aspects, named as UniCDR+. Our work was successfully deployed on the Kuaishou Living-Room RecSys.

翻译：针对领域专家推荐系统中长期存在的数据稀疏性和冷启动问题，跨域推荐（CDR）作为一种有前景的方法论应运而生。CDR旨在通过利用相关源域的交互知识（特别是跨越多域的用户或物品，例如短视频与客厅场景），提升目标域的预测性能。出于学术研究目的，存在多个不同维度指导CDR方法设计，包括辅助域数量、域重叠元素、用户-物品交互类型及下游任务。面对如此多样的CDR组合场景设置，现有场景专家方法通常针对特定垂直CDR场景定制，往往缺乏适应多重水平场景的能力。为协调适应多样化场景，并受领域不变迁移学习概念启发，我们在五个不同维度扩展了先前SOTA模型UniCDR，命名为UniCDR+。本研究成果已成功部署于快手客厅推荐系统。