Learning recommender systems with multi-class optimization objective is a prevalent setting in recommendation. However, as observed user feedback often accounts for a tiny fraction of the entire item pool, the standard Softmax loss tends to ignore the difference between potential positive feedback and truly negative feedback. To address this challenge, we propose a novel decoupled soft label optimization framework to consider the objectives as two aspects by leveraging soft labels, including target confidence and the latent interest distribution of non-target items. Futhermore, based on our carefully theoretical analysis, we design a decoupled loss function to flexibly adjust the importance of these two aspects. To maximize the performance of the proposed method, we additionally present a sensible soft-label generation algorithm that models a label propagation algorithm to explore users' latent interests in unobserved feedback via neighbors. We conduct extensive experiments on various recommendation system models and public datasets, the results demonstrate the effectiveness and generality of the proposed method.

翻译：学习具有多类优化目标的推荐系统是推荐领域中的一种普遍设置。然而，由于观测到的用户反馈通常仅占整个物品池的极小部分，标准的Softmax损失往往会忽略潜在的正反馈与真正的负反馈之间的差异。为了应对这一挑战，我们提出了一种新颖的解耦软标签优化框架，通过利用软标签（包括目标置信度和非目标物品的潜在兴趣分布）将目标视为两个方面。此外，基于我们细致的理论分析，我们设计了一种解耦损失函数，以灵活调整这两个方面的重要性。为了最大化所提出方法的性能，我们还提出了一种合理的软标签生成算法，该算法模拟了一种标签传播算法，通过邻居来探索用户在未观测反馈中的潜在兴趣。我们在多种推荐系统模型和公共数据集上进行了广泛的实验，结果证明了所提出方法的有效性和通用性。