In large-scale industrial e-commerce, the efficiency of an online recommendation system is crucial in delivering highly relevant item/content advertising that caters to diverse business scenarios. However, most existing studies focus solely on item advertising, neglecting the significance of content advertising. This oversight results in inconsistencies within the multi-entity structure and unfair retrieval. Furthermore, the challenge of retrieving top-k advertisements from multi-entity advertisements across different domains adds to the complexity. Recent research proves that user-entity behaviors within different domains exhibit characteristics of differentiation and homogeneity. Therefore, the multi-domain matching models typically rely on the hybrid-experts framework with domain-invariant and domain-specific representations. Unfortunately, most approaches primarily focus on optimizing the combination mode of different experts, failing to address the inherent difficulty in optimizing the expert modules themselves. The existence of redundant information across different domains introduces interference and competition among experts, while the distinct learning objectives of each domain lead to varying optimization challenges among experts. To tackle these issues, we propose robust representation learning for the unified online top-k recommendation. Our approach constructs unified modeling in entity space to ensure data fairness. The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations. Moreover, the proposed method balances conflicting objectives through the homoscedastic uncertainty weights and orthogonality constraints. Various experiments validate the effectiveness and rationality of our proposed method, which has been successfully deployed online to serve real business scenarios.

翻译：在大型工业级电子商务中，在线推荐系统的效率对于向多样化业务场景提供高度相关的商品/内容广告至关重要。然而，现有研究大多仅关注商品广告，忽视了内容广告的重要性。这种疏忽导致了多实体结构中的不一致性和不公平检索。此外，从不同领域的多实体广告中检索Top-K广告的挑战进一步增加了复杂性。近期研究表明，不同领域内的用户-实体行为展现出差异性和同质性特征。因此，多领域匹配模型通常依赖于混合专家框架，结合领域不变和领域特定的表示。不幸的是，多数方法主要聚焦于优化不同专家模块的组合方式，未能解决专家模块本身固有的优化难题。不同领域间存在的冗余信息会引入专家间的干扰与竞争，而各领域独特的学习目标又导致专家间存在差异化的优化挑战。为解决这些问题，我们提出了面向统一在线Top-K推荐的鲁棒表示学习。该方法在实体空间中构建统一建模以确保数据公平性。鲁棒表示学习采用领域对抗学习和多视角Wasserstein分布学习来获取鲁棒表示。此外，所提方法通过同方差不确定性权重与正交性约束平衡冲突目标。多项实验验证了我们方法的有效性与合理性，该方法已成功部署于线上为真实业务场景提供服务。