This paper introduces a novel approach for click-through rate (CTR) prediction within industrial recommender systems, addressing the inherent challenges of numerical imbalance and geometric asymmetry. These challenges stem from imbalanced datasets, where positive (click) instances occur less frequently than negatives (non-clicks), and geometrically asymmetric distributions, where positive samples exhibit visually coherent patterns while negatives demonstrate greater diversity. To address these challenges, we have used a deep neural network classifier that uses the polyhedral conic functions. This classifier is similar to the one-class classifiers in spirit and it returns compact polyhedral acceptance regions to separate the positive class samples from the negative samples that have diverse distributions. Extensive experiments have been conducted to test the proposed approach using state-of-the-art (SOTA) CTR prediction models on four public datasets, namely Criteo, Avazu, MovieLens and Frappe. The experimental evaluations highlight the superiority of our proposed approach over Binary Cross Entropy (BCE) Loss, which is widely used in CTR prediction tasks.

翻译：本文针对工业推荐系统中点击率预测所固有的数值不平衡与几何不对称挑战，提出了一种创新性解决方案。这些挑战源于数据集的固有特性：正样本（点击实例）的出现频率显著低于负样本（未点击实例），以及几何分布的不对称性——正样本通常呈现出视觉上连贯的模式，而负样本则表现出更高的多样性。为应对这些挑战，我们采用了一种基于多面体锥形函数的深度神经网络分类器。该分类器在理念上类似于单类分类器，通过生成紧凑的多面体接受区域，将正类样本与分布多样的负类样本进行有效分离。我们在四个公开数据集（Criteo、Avazu、MovieLens和Frappe）上，利用最先进的点击率预测模型进行了大量实验以验证所提方法的有效性。实验评估结果表明，我们提出的方法在性能上显著优于点击率预测任务中广泛使用的二元交叉熵损失函数。