通过反事实对比学习，提升推荐系统中数值特征的可解释性与效果
Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples
- 构建反事实样本并进行对比学习，建模数值特征与输出间的单调关系
- 在公开数据集和工业数据集上均显著提升推荐性能，支持大规模部署
- 适用于追求可解释性与高精度的工业级推荐系统场景
我们提出一种通用的、模型无关的反事实样本生成与对比学习框架（CCSS），用于建模神经网络输出与数值特征之间的单调关系，这对推荐系统的可解释性和有效性至关重要。CCSS采用两阶段流程：生成反事实样本，并对这些样本进行对比学习。该方法自然融入模型无关框架，实现端到端训练。我们在公开数据集和真实工业数据集上进行了大量实验，结果充分证明了CCSS的有效性。此外，该方法已成功部署于我方大规模工业推荐系统，服务超亿级用户。
原文摘要 · Abstract (English)
We propose a general model-agnostic Contrastive learning framework with Counterfactual Samples Synthesizing (CCSS) for modeling the monotonicity between the neural network output and numerical features which is critical for interpretability and effectiveness of recommender systems. CCSS models the monotonicity via a two-stage process: synthesizing counterfactual samples and contrasting the counterfactual samples. The two techniques are naturally integrated into a model-agnostic framework, forming an end-to-end training process. Abundant empirical tests are conducted on a publicly available dataset and a real industrial dataset, and the results well demonstrate the effectiveness of our proposed CCSS. Besides, CCSS has been deployed in our real large-scale industrial recommender, successfully serving over hundreds of millions users.
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