用于在线推荐的概率残差学习
Probabilistic Residual Learning for Online Recommendations
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中文总结 AI 辅助
针对现代推荐系统存在的问题，提出概率残差学习（PRL）方法，通过对真实值与基础预测间残差建模，概率性分组用户、建模混杂因素并聚合残差预测，与多种深度学习推荐系统兼容，能提升性能并发现有意义用户聚类。
中文摘要 AI 辅助
现代推荐系统通常基于深度学习模型，其中密集编码器学习用户和项目的表示。这些系统常受基础模型的黑箱性质和计算复杂性困扰，难以系统增强推荐能力。为此提出概率残差学习（PRL），一种因果贝叶斯推荐模型，对真实值与基础预测间的残差建模，实现对现有系统的定向优化。具体而言，PRL 概率性地对用户分组进行局部残差建模，对影响用户和项目表示的领域级混杂因素建模，并使用 do 演算在混杂因素上聚合特定聚类的残差预测。实验表明，即插即用的 PRL 与各种基础深度学习推荐系统兼容，提高其性能并自动发现有意义的用户聚类。
英文摘要
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.
发表机构
- Rutgers University Piscataway New Jersey United States（Meta）
- Meta Sunnyvale California United States
- University of Copenhagen Copenhagen Denmark
- Rutgers University \& UIUC Piscataway New Jersey United States
- Rutgers University
- Meta
- University of Copenhagen
- Rutgers University \& UIUC
机构由 AI 辅助整理，请以论文原文为准。
