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.

本文介绍了一种新颖的工业推荐系统中点击率（CTR）预测方法，解决了数值不平衡和几何不对称的固有挑战。