WSDM 2023 推荐系统相关论文整理（wsdm 2022 结果）

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WSDM 2023的论文录用结果已出，以下是论文列表地址，笔者整理了推荐系统相关的一些论文，包含序列推荐，点击率估计，多样性等领域，涵盖图学习，对比学习，因果推断，知识蒸馏等技术抓紧学起来吧https://www.wsdm-conference.org/2023/program/accepted-papers。

【哈工程】Disentangled Negative Sampling for Collaborative Filtering【解耦负采样的协同过滤】【新南威尔士】IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation【IDNP：使用生成神经过程进行序列推荐的兴趣动态建模】

【上交，移动】Learning to Distinguish Multi-User Coupling Behaviors for TV Recommendation【学习区分电视推荐中的多用户耦合行为】

【阿尔伯塔，腾讯】One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation【迁移学习跨域推荐】

【腾讯】Slate-Aware Ranking for Recommendation【南航】Knowledge Enhancement for Contrastive Multi-Behavior Recommendation【对比多行为推荐系统】

【字节】Disentangled Representation for Diversified Recommendations【多样性推荐中解耦表征】【人大】Cognition-aware Knowledge Graph Reasoning for Explainable Recommendation【可解释推荐的认知知识图推理】

【中山】Self-Supervised Group Graph Collaborative Filtering for Group Recommendation【群组推荐的自监督群图协同过滤】【spotify】Calibrated Recommendations as a Maximum Flow Problem【作为最大流量问题的校准化推荐】

【北大，美团】DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation【DisenPOI：解耦POI推荐中序列和地理的影响】

【天大】Multi-Intentions Oriented Contrastive Learning for Sequential Recommendation【面向多意图的序列推荐对比学习】【南大，阿里】MUSENET: Multi-Scenario Learning for Repeat-Aware Personalized Recommendation【MUSENET：重复感知的个性化推荐的多场景学习】

【阿姆斯特丹大学】A Personalized Neighborhood-based Model for Within-basket Recommendation in Grocery Shopping【杂货店购物篮内推荐的个性化邻域模型】

【悉尼科技大学】SGCCL: Siamese Graph Contrastive Consensus Learning for Personalized Recommendation【SGCCL：用于个性化推荐的暹罗图对比共识学习】

【武大】Relation Preference oriented High-order Sampling for Recommendation【面向关系偏好的高阶采样】【山大】Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation【会话推荐中不完全知识图的变分推理】

【悉尼科技大学】Exploiting Explicit and Implicit Item relationships for Session-based Recommendation【利用基于会话的推荐的显式和隐式商品关系】

Range Restricted Route Recommendation Based on Spatial Keyword【基于空间关键词的距离受限路径推荐】【弗吉尼亚，京东】Meta Policy Learning for Cold-Start Conversational Recommendation【元学习做对话推荐的冷启动】

【港大，微软】Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network【通过Atten Mixer网络有效利用多级用户意图进行基于会话的推荐】

【北大】Improving News Recommendation with Channel-Wise Dynamic Representations and Contrastive User Modeling【利用频道动态表征和对比用户模型改进新闻推荐】

【悉尼科技大学，ebay】Simplifying Graph-based Collaborative Filtering for Recommendation【简化基于图的推荐协同过滤】【上交，华为】AutoGen: An Automated Dynamic Model Generation Framework for Recommender System【AutoGen:推荐系统的自动动态模型生成框架】

【浙大，meta】A Causal View for Item-level Effect of Recommendation on User Preference【对用户偏好影响的因果分析】【昆士兰，多家公司】Federated Unlearning for On-Device Recommendation【联邦unlearning】

【罗格斯】Explicit Counterfactual Data Augmentation for Recommendation【反事实数据增强推荐系统】【康奈尔】Uncertainty Quantification for Fairness in Two-Stage Recommender Systems【两阶段推荐系统公平性的不确定性量化】

【伊利诺，字节】DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation【DGRec：具有多种embedding生成的用于推荐的图神经网络】

【中科大】Unbiased Knowledge Distillation for Recommendation【无偏知识蒸馏推荐系统】【华中科技】VRKG4Rec: Virtual Relational Knowledge Graph for Recommendation【VRKG4Rec：用于推荐的虚拟关系知识图】

【阿里，北邮】Knowledge-Adaptive Contrastive Learning for Recommendation【知识自适应对比学习推荐系统】【华南理工】Heterogeneous Graph Contrastive Learning for Recommendation【面向推荐的异构图对比学习】

【上交，华为】An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages【多块移动页面信息检索的F形点击模型】

【人大，华为】Separating Examination and Trust Bias from Click Predictions for Unbiased Relevance Ranking【从点击预测中分离检查和信任偏差，进行无偏关联排序】

【复旦，微软】CL4CTR: A Contrastive Learning Framework for CTR Prediction【CL4CTR:CTR预测的对比学习框架】【人大，腾讯】Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation【基于知识提取的CTR预测的有向非循环图分解机】

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