用概率残差学习提升推荐系统可解释性与效果
Probabilistic Residual Learning for Online Recommendations
- 通过概率分组用户，局部建模预测偏差
- 引入领域混淆因子，提升推荐准确性
- 兼容主流推荐模型，自动发现用户群组
现代推荐系统多基于深度学习模型，其密集编码器虽能学习用户和物品表征，但存在黑箱性强、计算复杂等问题，难以系统性提升推荐能力。为此，我们提出概率残差学习（PRL），一种因果贝叶斯推荐模型，通过建模真实值与基线预测之间的残差，实现对现有系统的针对性优化。PRL（1）以概率方式对用户分组，支持局部残差建模；（2）建模影响用户和物品表征的领域级混淆因子；（3）利用do-演算在混淆因子上聚合各簇残差预测。实验表明，PRL可无缝集成至多种基线深度学习推荐系统，在不改变原结构的前提下提升性能，并自动发现有意义的用户群体。
原文摘要 · Abstract (English)
Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a result, these systems often suffer from the black-box nature and computational complexity of the underlying models, making it difficult to systematically enhance their recommendation capabilities. To address this problem, we propose Probabilistic Residual Learning (PRL), a causal Bayesian recommendation model that models the residual between ground-truth and base predictions, enabling targeted refinement of existing systems. Specifically, PRL (1) probabilistically groups users for localized residual modeling, (2) models domain-level confounders that influence user and item representations, and (3) aggregates cluster-specific residual predictions over the confounders using do-calculus. Experiments demonstrate that our plug-and-play PRL is compatible with various base deep learning recommender systems, improving their performance while automatically discovering meaningful user clusters.
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