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WWW 2023接收论文已经放出来啦，笔者这里主要总结了推荐系统和因果推断相关的论文。此次，图神经网络，跨域推荐，冷启动依旧是热点，当然还包括很多类型：自动机器学习，神经架构搜索NAS，因果推断，强化学习，公平性，还有prompt相关内容，感兴趣的小伙伴可以看看。
所有论文地址：https://www2023.thewebconf.org/program/accepted-papers/
推荐相关
Learning with Exposure Constraints in Recommendation Systems【在推荐系统中通过对曝光就行约束来学习】
Submodular Maximization in the Presence of Biases with Applications to Recommendation【存在偏差时的子模块最大化与推荐系统中的应用】
Scoping Fairness Objectives and Identifying Fairness Metrics for Recommender Systems: The Practitioners’ Perspective【推荐系统公平性目标和公平性指标】
P-MMF: Provider Max-min Fairness Re-ranking in Recommender System【P-MMF：推荐系统中供给方最大公平性重排】
Fairly Adaptive Negative Sampling for Recommendations【推荐系统中的公平自适应负采样】
RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems【RL-MPCA：一种基于强化学习的推荐系统多阶段计算分配方法】
Dynamically Expandable Graph Convolution for Streaming Recommendation【用于流式推荐的动态可扩展图卷积】
Dual Policy Learning for Aggregation Optimization in Recommender Systems【推荐系统中聚合优化的双策略学习】
Automatic Feature Selection By One-Shot Neural Architecture Search In Recommendation Systems【one
-shot神经架构搜索自动选择特征】
Semi-supervised Adversarial Learning for Complementary Item Recommendation【互补商品推荐的半监督对抗学习】
Recommendation with Causality enhanced Natural Language Explanations【因果关系增强自然语言解释的推荐】
Enhancing User Personalization in Conversational Recommenders【增强对话式推荐器中的用户个性化】
LINet: A Location and Intention-Aware Neural Network for Hotel Group Recommendation【LINet：用于酒店group推荐的位置和意图感知神经网络】
Multi-Modal Adversarial Self-Supervised Learning for Recommendation【多模态对抗自监督学习推荐】
Distillation from Heterogeneous Models for Top-K Recommendation【从异构模型中蒸馏用于 top-K 推荐】
On the Theories Behind Hard Negative Sampling for Recommendation【负采样背后的理论】
Fine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation【微调分区感知商品的相似性，以实现高效且可扩展的推荐】
Exploration and Regularization of the Latent Action Space in Recommendation【推荐中潜在动作空间的探索与正则化】
Bootstrap Latent Representations for Multi-modal Recommendation【多模态推荐的潜在表示】
Two-Stage Constrained Actor-Critic for Short Video Recommendation【短视频推荐中的两阶段约束AC强化学习】
Cross-domain recommendation via user interest alignment【通过用户兴趣对齐进行跨域推荐】
A Simple Data-Augmented Framework For Smoothed Recommender System【一种用于平滑推荐系统的简单数据增强框架】
Dual-interest Factorization-heads Attention for Sequential Recommendation【双重兴趣因子分解用于序列推荐】
Contrastive Collaborative Filtering for Cold-Start Item Recommendation【冷启动推荐的对比协同过滤】
Anti-FakeU: Defending Shilling Attacks on Graph Neural Network based Recommender Model【Anti-FakeU：基于图神经网络的推荐模型防御Shilling攻击】
Compressed Interaction Graph based Framework for Multi-behavior Recommendation【基于压缩交互图的多行为推荐框架】
A Counterfactual Collaborative Session-based Recommender System【一种基于反事实协作的会话推荐系统】
Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation【基于层次超图网络的偏好迁移的多域推荐】
Automated Self-Supervised Learning for Recommendation with Masked Graph Transformer【基于mask图transformer的自动自监督推荐学习】
Improving Recommendation Fairness via Data Augmentation【通过数据增强提高推荐公平性】
ColdNAS: Search to Modulate for User Cold-Start Recommendation【ColdNAS：搜索以调节用户冷启动推荐】
AutoS2AE: Automate to Regularize Sparse Shallow Autoencoders for Recommendation【AutoS2AE：自动规则化稀疏浅层自动编码器以供推荐】
Quantize Sequential Recommenders Without Private Data【量化序列推荐】
Interaction-level Membership Inference Attack Against Federated Recommender Systems【针对联邦推荐系统的交互级成员推断攻击】
Contrastive Learning with Interest and Conformity Disentanglement for Sequential Recommendation【具有兴趣和从众性的序列推荐的对比学习】
Clustered Embedding Learning for Large-scale Recommender Systems【大规模推荐系统的集群embedding学习】
CAMUS: Attribute-Aware Counterfactual Augmentation for Minority Users in Recommendation【CAMUS：推荐中针对少数类用户的属性感知反事实增强】
Confident Action Decision via Hierarchical Policy Learning for Conversational Recommendation【基于层次策略学习的会话推荐可信动作决策】
Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation【序列推荐的相互Wasserstein差异最小化】
Denoising and Prompt-Tuning for Multi-Behavior Recommendation【多行为推荐的去噪和提示调整】
Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations【在无偏推荐中平衡未观察到的混淆与少数无偏见的打分】
Cross-domain Recommendation with Behavioral Importance Perception【具有行为重要性感知的跨领域推荐】
A Self-Correcting Sequential Recommender【一种自校正序列推荐器】
Multi-Task Recommendations with Reinforcement Learning【具有强化学习的多任务推荐】
Modeling Temporal Positive and Negative Excitation for Sequential Recommendation【序列推荐的时间正激励和负激励建模】
Communicative MARL-based Relevance Discerning Network for Repetition-Aware Recommendation【基于交际MARL的重复感知推荐关联识别网络】
Membership Inference Attacks Against Sequential Recommender Systems【针对序列推荐系统的成员推断攻击】
NASRec: Weight Sharing Neural Architecture Search for Recommender Systems【NASRec：推荐系统的权重共享神经结构搜索】
AutoMLP: Automated MLP for Sequential Recommendations【AutoMLP：用于序列推荐的自动MLP】
Multi-Behavior Recommendation with Cascading Graph Convolutional Network【级联图卷积网络的多行为推荐】
Show Me The Best Outfit for A Certain Scene: A Scene-aware Fashion Recommender System【一个场景感知的时尚推荐系统】
Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders【可迁移序列推荐的学习向量量化商品表示】
Cooperative Retriever and Ranker in Deep Recommenders
User Retention-oriented Recommendation with Decision Transformer【基于决策transformer的面向用户留存的推荐】
Few-shot News Recommendation via Cross-lingual Transfer【通过跨语言迁移的few-shot新闻推荐】
MMMLP: Multi-modal Multilayer Perceptron for sequence recommendation【MMMLP：用于序列推荐的多模态多层感知器】
Robust Preference-Guided Denoising for Graph based Social Recommendation【基于图的社交推荐的鲁棒偏好引导去噪】
Adap-: Adpatively Modulating Embedding Magnitude for Recommendation【用于推荐的自适应调制embedding幅度】
IDPN: Instance Denoising for Click-through Rate Prediction【IDPN：点击率预测的实例去噪】
Collaboration-Aware Graph Convolutional Network for Recommender Systems【用于推荐系统的协作感知图卷积网络】
Enhancing Hierarchy-Aware Graph Networks with Deep Dual Clustering for Session-based Recommendation【利用深度对偶聚类增强层次感知图网络的会话推荐】
ConsRec: Learning Consensus Behind Interactions for Group Recommendation【ConsRec：group推荐互动背后的学习共识】
Semi-decentralized Federated Ego Graph Learning for Recommendation【用于推荐的半去中心化联邦图学习】
Joint Internal Multi-Interest Exploration and External Domain Alignment for Cross Domain Sequential Recommendation【跨域序列推荐的内部多兴趣联合探索和外部领域对齐】
Intra and Inter Domain HyperGraph Convolutional Network for Cross-Domain Recommendation【用于跨域推荐的域内和域间超图卷积网络】
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation【用于基于会话的推荐的双意图增强图神经网络】
ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation【ApeGNN：用于推荐的GNN中的节点自适应聚合】
因果
Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning【基于辅助图结构学习的路径特定因果公平预测】
Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling【基于图神经网络和两个uplift估计器对标签稀缺性的个体建模】
CausIL: Causal Graph for Instance Level Microservice Data【CaustIL：实例级微服务数据的因果图】
Recommendation with Causality enhanced Natural Language Explanations【具有因果关系增强的自然语言解释的推荐】
Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations【在无偏推荐中平衡未观察到的混淆与少数无偏见的打分】
Knowledge Graph Completion with Counterfactual Augmentation【具有反事实增广的知识图补全】
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning【用于图对比学习的反事实难负样本的生成】
A Counterfactual Collaborative Session-based Recommender System【一种基于反事实协作的会话推荐系统】
CAMUS: Attribute-Aware Counterfactual Augmentation for Minority Users in Recommendation【CAMUS：推荐中针对长尾用户的属性感知反事实增强】
Towards Model Robustness: Generating Contextual Counterfactuals for Entities in Relation Exaction【走向模型稳健性：在关系检验中为实体生成上下文反事实】
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