Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglecting the importance of training schemes. This work revisits the training framework and uncovers two major problems hindering the expressiveness of learned multi-interest representations. First, the current training objective (i.e., uniformly sampled softmax) fails to effectively train discriminative representations in a multi-interest learning scenario due to the severe increase in easy negative samples. Second, a routing collapse problem is observed where each learned interest may collapse to express information only from a single item, resulting in information loss. To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method. IHN emphasizes interest-aware hard negatives by proposing an ideal sampling distribution and developing a Monte-Carlo strategy for efficient approximation. RR prevents routing collapse by introducing a novel regularization term on the item-to-interest routing matrices. These two components enhance the learned multi-interest representations from both the optimization objective and the composition information. REMI is a general framework that can be readily applied to various existing multi-interest candidate matching methods. Experiments on three real-world datasets show our method can significantly improve state-of-the-art methods with easy implementation and negligible computational overhead. The source code will be released.

翻译：现有研究在推荐系统多兴趣候选匹配方面主要集中于改进模型架构或融合额外信息，忽略了训练方案的重要性。本文重新审视了训练框架，揭示了阻碍多兴趣表示表达能力的两大问题。第一，当前训练目标（即均匀采样的 softmax）在多兴趣学习场景中因简单负样本的急剧增加而无法有效训练判别性表示。第二，观察到路由坍缩问题，即每个学习到的兴趣可能坍缩为仅表达来自单个物品的信息，导致信息损失。为解决这些问题，我们提出了 REMI 框架，包含兴趣感知难负样本挖掘策略（IHN）和路由正则化方法（RR）。IHN 通过提出理想采样分布并开发蒙特卡洛策略进行高效近似，强调兴趣感知的难负样本。RR 通过在物品到兴趣路由矩阵上引入新颖的正则化项，防止路由坍缩。这两部分分别从优化目标和组成信息两方面增强了学习到的多兴趣表示。REMI 是一个通用框架，可便捷应用于多种现有多兴趣候选匹配方法。在三个真实世界数据集上的实验表明，我们的方法能够显著提升现有最先进方法的表现，且易于实现、计算开销极小。源代码将公开发布。