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KDD 2023的论文接受列表已出，这里给大家整理了推荐系统相关的文章，包含的类别有很多，包括对话推荐，用户隐私，公平性，纠偏，如何将语言模型用于推荐，多场景推荐等等。
地址：https://kdd.org/kdd2023/research-track-papers/
https://kdd.org/kdd2023/ads-track-papers/
其中也不乏我们之前已经阅读的文章，感兴趣的同学可以康康：
KDD'23 谷歌 | CDN：交叉解耦网络来应对长尾分布的item推荐
KDD'23「Amazon」Text Is All You Need：通过学习语言表征来用于序列推荐
| Improving Conversational Recommendation Systems via Counterfactual Data Simulation【人大，华为】 |
| LATTE: A Framework for Learning Item-Features to Make a Domain-Expert for Effective Conversational Recommendation【汉阳，KT】 |
| Delving into Global Dialogue Structures: Structure Planning Augmented Response Selection for Multi-turn Conversations【人大】 |
| User-Regulation Deconfounded Conversational Recommender System with Bandit Feedback【纽约大学，adobe】 |
| Path-Specific Counterfactual Fairness for Recommender Systems【弗吉尼亚大学，领英】 |
| Meta Multi-agent Exercise Recommendation: A Game Application Perspective【合肥大学】 |
| Shilling Black-box Review-based Recommender Systems through Fake Review Generation【清华】 |
| Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation【剑桥，港大，腾讯】 |
| Generative Flow Network for Listwise Recommendation【快手】 |
| PSLOG: Pretraining with Search Logs for Document Ranking【人大】 |
| Text Is All You Need: Learning Language Representations for Sequential Recommendation【亚马逊】 |
| MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction【交大，华为】 |
| Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction【中科大】 |
| PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement【南洋理工，快手】 |
| Efficient Bi-Level Optimization for Recommendation Denoising【重大】 |
| Adaptive Disentangled Transformer for Sequential Recommendation【清华】 |
| Meta Graph Learning for Long-tail Recommendation【清华，阿里】 |
| Graph Neural Bandits |
| E-commerce Search via Content Collaborative Graph Neural Network【厦大，阿里】 |
| Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation【延世大学】 |
| Knowledge Graph Self-Supervised Rationalization for Recommendation【港大，腾讯】 |
| On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering【北大，微软】 |
| Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay【spotify】 |
| Hierarchical Invariant Learning for Domain Generalization Recommendation【人大】 |
| UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation【加州大学】 |
| Debiasing Recommendation by Learning Identifiable Latent Confounders【港科大，字节】 |
| Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective【浙大】 |
| Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation【北大】 |
| Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction【清华】 |
| A Sublinear Time Algorithm for Opinion Optimization in Directed Social Networks via Edge Recommendation【复旦】 |
| Contrastive Learning for User Sequence Representation in Personalized Product Search【人大】 |
| A Collaborative Transfer Learning Framework for Cross-domain Recommendation【美团】 |
| Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop【华为，交大】 |
| UA-FedRec: Untargeted Attack on Federated News Recommendation【中科大，微软】 |
| PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation【人大】 |
| Doctor Specific Tag Recommendation for Online Medical Record Management【香港城市大学】 |
| Hierarchical Projection Enhanced Multi-behavior Recommendation【清华，华为】 |
| Improving Training Stability for Multitask Ranking Models in Recommender Systems【谷歌】 |
| AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations【meta】 |
| SAMD: An Industrial Framework for Heterogeneous Multi-Scenario Recommendation【蚂蚁】 |
| TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest【pinterest】 |
| Controllable Multi-Objective Re-ranking with Policy Hypernetworks【阿里】 |
| M5: Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation【hulu】 |
| CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation【腾讯，oppo】 |
| Multi-channel Integrated Recommendation with Exposure Constraints【阿里】 |
| Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems【字节，apple】 |
| On-device Integrated Re-ranking with Heterogeneous Behavior Modeling【交大，华为】 |
| Variance Reduction Using In-Experiment Data: Efficient and Targeted Online Measurement for Sparse and Delayed Outcomes【airbnb】 |
| Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation【谷歌】 |
| VRDU: A Benchmark for Visually-rich Document Understanding【谷歌】 |
| PGLBox: Multi-GPU Graph Learning Framework for Web-Scale Recommendation【百度】 |
| Counterfactual Video Recommendation for Duration Debiasing【清华】 |
| Exploiting Intent Evolution in E-commercial Query Recommendation【亚马逊】 |
| Workplace Recommendation with Temporal Network Objectives【微软，康奈尔】 |
| A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification【华为】 |
| Modeling Dual Period-Varying Preferences for Takeaway Recommendation【国科大】 |
| SentiGOLD: A Large Bangla Gold Standard Multi-Domain Sentiment Analysis Dataset and its Evaluation【giga】 |
| Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)【谷歌】 |
| Stationary Algorithmic Balancing Over Dynamic Email Re-Ranking Problem【普渡大学，微软】 |
| Revisiting Neural Retrieval on Accelerators【meta】 |
| Contrastive Learning of Stress-specific Word Embedding for Social Media based Stress Detection【清华】 |
| Adaptive Graph Contrastive Learning for Recommendation【港大】 |
| BOSS: A Bilateral Occupational-Suitability-Aware Recommender System for Online Recruitment【boss直聘】 |
| Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation【快手】 |
| PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce【美团】 |
| Constrained Social Community Recommendation【港中文，腾讯】 |
| Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction【微信】 |
| TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou【快手】 |
| BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction【微软】 |
| Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach【阿里】 |
| Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction【清华，华为】 |
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