NIPS 2022 推荐系统，因果效应论文集锦

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NIPS 2022的录用情况已经公布了，这里笔者汇总了其中推荐系统和因果效应相关的论文，希望对大家有所帮助。

推荐系统

DreamShard: Generalizable Embedding Table Placement for Recommender Systems【DreamShard：推荐系统的通用embedding表】

RecZilla: Algorithm Selection for Recommender Systems

Contrastive Graph Structure Learning via Information Bottleneck for Recommendation【基于信息瓶颈的对比图结构学习】

Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems【推荐系统中具有点击后信息的广义延迟反馈模型】

Diversified Recommendations for Agents with Adaptive Preferences【针对具有适应性偏好的代理的多样化推荐系统】

Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever【批内重要性重采样】

Infinite Recommendation Networks: A Data-Centric Approach【无限推荐网络：一种以数据为中心的方法】

Revisiting Injective Attacks on Recommender Systems【重新审视对推荐系统的注入式攻击】

Debiased, Longitudinal and Coordinated Drug Recommendation through Multi-Visit Clinic Records【去偏药物推荐】

GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Models【GBA：在推荐模型的同步和异步训练之间切换的免调优方法】

The trade-offs of model size in large recommendation models : A 10000 ×× compressed criteo-tb DLRM model (100 GB parameters to mere 10MB)【大型推荐模型中模型大小的权衡】

Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy【基于图卷积网络的推荐系统：学习保证和商品混合驱动策略】

Recommender Forest for Efficient Retrieval【高效检索的推荐系统森林】

Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems【Tenrec：推荐系统的大规模多用途基准数据集】

APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction【APG：点击率预测的自适应参数生成网络】

因果效应

Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies【非结构代理因果效应估计的深度多模结构方程】

Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders【在未观察到的混淆者存在的情况下，从干预措施中分离解耦因果效应】

CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior【CEBaB：估计真实世界概念对NLP模型行为的因果关系】

An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects【异质因果联邦学习的自适应核方法】

Generalization Bounds for Estimating Causal Effects of Continuous Treatments【估计连续干预因果效应的推广界】

Falsification before Extrapolation in Causal Effect Estimation【因果关系估计】

Learning Individualized Treatment Rules with Many Treatments: A Supervised Clustering Approach Using Adaptive Fusion【用多干预方法学习个体化干预规则：一种基于自适应融合的监督聚类方法】

Partial Identification of Treatment Effects with Implicit Generative Models

Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding【去偏因果树：未测量混淆的异质治疗效果评估】

Sample Constrained Treatment Effect Estimation【样本约束的干预效果估计】

Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation【基于异质特征空间的迁移学习用于干预效果评估】

Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability【从可解释性的角度对非均质干预效果模型进行基准测试】