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CIKM 2022的论文已出，笔者整理了其中的推荐系统，点击率以及因果推断相关的论文。今年跨域，图学习还是热点，点击率模型中长短期兴趣的处理是热点。
A Case Study in Educational Recommenders:Recommending Music Partitures at Tomplay【在 Tomplay 推荐音乐片段】
A Multi-Interest Evolution Story: Applying Psychology in Query-based Recommendation for Inferring Customer Intention【在基于查询的推荐中应用心理学以推断客户意图】
A Relevant and Diverse Retrieval-enhanced Data Augmentation Framework for Sequential Recommendation【用于序列推荐的相关且多样化的检索增强数据增强框架】
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation【个性化推荐】
Adaptive Domain Interest Network for Multi-domain Recommendation【用于多域推荐的自适应域兴趣网络】
Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation【大规模推荐的神经相似度度量下的近似最近邻搜索】
Asymmetrical Context-aware Modulation for Collaborative Filtering Recommendation【用于协同过滤推荐的非对称上下文感知调制】
AutoMARS: Searching to Compress Multi-Modality Recommendation Systems【AutoMARS：搜索压缩多模态推荐系统】
Automatic Meta-Path Discovery for Effective Graph-Based Recommendation【基于图的有效推荐的自动元路径发现】
Best practices for top-N recommendation evaluation: Candidate set sampling and Statistical inference techniques【top-N 推荐评估的最佳实践：候选集抽样和统计推断技术】
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability【通过个性化兴趣可持续性的序列推荐】
CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems【CROLoss：迈向推荐系统中检索模型的可定制损失】
ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation【ContrastVAE：用于序列推荐的对比变分自动编码器】
Contrastive Cross-Domain Sequential Recommendation【对比跨域序列推荐】
Contrastive Learning with Bidirectional Transformers for Sequential Recommendation【用于序列推荐的双向 Transformer 对比学习】
Cross-domain Recommendation via Adversarial Adaptation【通过对抗性适应进行跨域推荐】
DeepVT: Deep View-Temporal Interaction Network for News Recommendation【DeepVT：新闻推荐的深度视图-时间交互网络】
Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks【通过双网络解耦序列推荐中的过去未来】
Dual-Task Learning for Multi-Behavior Sequential Recommendation【多行为序列推荐的双任务学习】
Dually Enhanced Propensity Score Estimation in Sequential Recommendation【序列推荐中的双重增强倾向得分估计】
Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation【通过图嵌套 GRU ODE 进行进化偏好学习，用于基于会话的推荐】
Explanation Guided Contrastive Learning for Sequential Recommendation【序列推荐的解释引导对比学习】
FedCDR: Federated Cross-Domain Recommendation for Privacy-Preserving Rating Prediction【FedCDR：用于隐私保护评级预测的联合跨域推荐】
GBERT: Pre-training User representations for Ephemeral Group Recommendation【GBERT：为临时组推荐预训练用户表示】
GRP: A Gumbel-based Rating Prediction Framework for Imbalanced Recommendation【GRP：基于 Gumbel 的不平衡推荐评级预测框架】
Generative Adversarial Zero-Shot Learning for Cold-Start News Recommendation【冷启动新闻推荐的生成对抗零样本学习】
Gromov-Wasserstein Guided Representation Learning for Cross-Domain Recommendation【Gromov-Wasserstein 引导表征学习的跨域推荐】
Hierarchical Item Inconsistency Signal learning for Sequence Denoising in Sequential Recommendation【序列推荐中序列去噪的分层商品不一致信号学习】
HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations【HySAGE：用于上下文漂移推荐的混合静态和自适应图嵌入网络】
Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning【通过多层次交互式对比学习改进知识感知推荐】
Improving Text-based Similar Product Recommendation for Dynamic Product Advertising at Yahoo【改进雅虎动态产品广告的基于文本的相似产品推荐】
Knowledge Enhanced Multi-Interest Network for the Generation of Recommendation Candidates【用于生成推荐候选的知识增强多兴趣网络】
Knowledge Extraction and Plugging for Online Recommendation【在线推荐的知识抽取与插入】
KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems【KuaiRec：用于评估推荐系统的完全观察数据集和见解】
Leveraging Multiple Types of Domain Knowledge for Safe and Effective Drug Recommendation【利用多种领域知识进行安全有效的药物推荐】
MARIO: Modality-Aware Attention and Modality-Preserving Decoders for Multimedia Recommendation【MARIO：用于多媒体推荐的模态感知注意力和模态保留解码器】
MIC:Model-agnostic Integrated Cross-channel Recommender【MIC：与模型无关的集成跨渠道推荐系统】
Memory Bank Augmented Long-tail Sequential Recommendation【记忆库增强长尾序列推荐】
Multi-Aggregator Time-Warping Heterogeneous Graph Neural Network for Personalized Micro-video Recommendation【用于个性化微视频推荐的多聚合器时间扭曲异构图神经网络】
Multi-Faceted Hierarchical Multi-Task Learning for Recommender Systems【推荐系统的多方面分层多任务学习】
Multi-level Contrastive Learning Framework for Sequential Recommendation【序列推荐的多层次对比学习框架】
Multimodal Meta-Learning for Cold-Start Sequential Recommendation【冷启动序列推荐的多模态元学习】
PROPN: Personalized Probabilistic Strategic Parameter Optimization in Recommendations【PROPN：推荐中的个性化概率策略参数优化】
PlatoGL: Effective and Scalable Deep Graph Learning System for Graph-enhanced Real-Time Recommendation【PlatoGL：用于图增强实时推荐的有效且可扩展的深度图学习系统】
Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems【量化和减轻会话推荐系统中的流行度偏差】
Rank List Sensitivity of Recommender Systems to Interaction Perturbations【推荐系统对交互扰动的排名列表敏感性】
Real-time Short Video Recommendation on Mobile Devices【移动端实时短视频推荐】
Representation Matters When Learning From Biased Feedback in Recommendation【从推荐中的有偏偏反馈中学习时，表征很重要】
Rethinking Conversational Recommendations: Is Decision Tree All You Need?【重新思考对话推荐：决策树！！】
Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation【跨域推荐】
SASNet: Stage-aware sequential matching for online travel recommendation【SASNet：在线旅游推荐的阶段感知序列匹配】
SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation【SVD-GCN：用于推荐的简化图卷积范式】
Scenario-Adaptive and Self-Supervised Model for Multi-Scenario Personalized Recommendation【多场景个性化推荐的场景自适应自监督模型】
Spatiotemporal-aware Session-based Recommendation with Graph Neural Networks【使用图神经网络的时空感知基于会话的推荐】
Storage-saving Transformer for Sequential Recommendations【用于序列推荐的节省存储的Transformer】
Target Interest Distillation for Multi-Interest Recommendation【多兴趣推荐的目标兴趣蒸馏】
Temporal Contrastive Pre-Training for Sequential Recommendation【序列推荐的时间对比预训练】
The Interaction Graph Auto-encoder Network Based on Topology-aware for Transferable Recommendation【基于拓扑感知的可迁移推荐交互图自动编码器网络】
Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation【Tiger：用于域级零样本推荐的可迁移兴趣图嵌入】
Time Lag Aware Sequential Recommendation【延时感知序列推荐】
Towards Principled User-side Recommender Systems【迈向有原则的用户侧推荐系统】
Two-level Graph Path Reasoning for Conversational Recommendation with User Realistic Preference【具有用户现实偏好的会话推荐的两级图路径推理】
UDM: A Unified Deep Matching Framework in Recommender Systems【UDM：推荐系统中的统一深度匹配框架】
User Recommendation in Social Metaverse with VR【VR的用户推荐】
点击率估计
GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction【GIFT：用于冷启动视频点击率预测的图引导特征迁移】
Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction【用于点击率预测的基于图的长期和短期兴趣模型】
Hierarchically Fusing Long and Short-Term User Interests for Click-Through Rate Prediction in Product Search【分层融合长期和短期用户兴趣以进行产品搜索中的点击率预测】
OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction【OptEmbed：学习点击率预测的最优嵌入表】
Sparse Attentive Memory Network for Click-through Rate Prediction with Long Sequences【用于长序列点击率预测的稀疏注意力记忆网络】
Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Models【了解深度点击率模型的过拟合现象】
因果推断，因果效应
E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling【通过带有uplift建模的在线的电子商务促销个性化】
MEMENTO: Neural Model for Estimating Individual Treatment Effects for Multiple Treatments【MEMENTO：用于估计多干预的ITE的神经模型】
Adaptive Multi-Source Causal Inference from Observational Data【来自观测数据的自适应多源因果推断】
Bootstrap-based Causal Structure Learning【基于 Bootstrap 的因果结构学习】
Causal Learning Empowered OD Prediction for Urban Planning【因果学习赋能城市规划】
Causal Relationship over Knowledge Graphs【知识图谱上的因果关系】
Dynamic Causal Collaborative Filtering【动态因果协同过滤】
Estimating Causal Effects on Networked Observational Data via Representation Learning【通过表征学习估计网络观测数据的因果效应】
