KDD 2023 推荐系统论文梳理（71篇）_kdd论文

推荐系统（RS）主要是指应用协同智能（collaborative intelligence）做推荐的技术，解决了用户在面对大量信息时无法从中获得对自己真正有用的那部分信息的问题。

相较于搜索引擎，推荐系统可以根据用户的信息需求、兴趣等，将用户感兴趣的信息、产品等推荐给用户，非常的个性化。

目前，推荐系统已经广泛应用于很多领域，与之相关的研究成果也非常多，在今年的KDD 2023 会议录用论文中，与推荐系统相关的论文数目十分可观。

KDD 的含金量就不用多说了吧，今年的 KDD 2023 大会共公布了8篇获奖论文，有需要的同学点蓝字传送。

这次和大家分享的是KDD 2023 会议录用的71篇推荐系统论文，我把论文目录整理在下面了，有需要原文+代码合集的同学，文末领取。

推荐系统论文list：

• Improving Conversational Recommendation Systems via Counterfactual Data Simulation

• LATTE: A Framework for Learning Item-Features to Make a Domain-Expert for Effective Conversational Recommendation

• Delving into Global Dialogue Structures: Structure Planning Augmented Response Selection for Multi-turn Conversations

• User-Regulation Deconfounded Conversational Recommender System with Bandit Feedback

• Path-Specific Counterfactual Fairness for Recommender Systems

• Meta Multi-agent Exercise Recommendation: A Game Application Perspective

• Shilling Black-box Review-based Recommender Systems through Fake Review Generation

• Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation

• Generative Flow Network for Listwise Recommendation

• PSLOG: Pretraining with Search Logs for Document Ranking

• Text Is All You Need: Learning Language Representations for Sequential Recommendation

• MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction

• Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction

• PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement

• Efficient Bi-Level Optimization for Recommendation Denoising

• Adaptive Disentangled Transformer for Sequential Recommendation

• Meta Graph Learning for Long-tail Recommendation

• Graph Neural Bandits

• E-commerce Search via Content Collaborative Graph Neural Network

• Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation

• Knowledge Graph Self-Supervised Rationalization for Recommendation

• On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filtering

• Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay

• Hierarchical Invariant Learning for Domain Generalization Recommendation

• UCEpic: Unifying Aspect Planning and Lexical Constraints for Generating Explanations in Recommendation

• Debiasing Recommendation by Learning Identifiable Latent Confounders

• Reconsidering Learning Objectives in Unbiased Recommendation: A Distribution Shift Perspective

• Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation

• Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction

• A Sublinear Time Algorithm for Opinion Optimization in Directed Social Networks via Edge Recommendation

• Contrastive Learning for User Sequence Representation in Personalized Product Search

• A Collaborative Transfer Learning Framework for Cross-domain Recommendation

• Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop

• UA-FedRec: Untargeted Attack on Federated News Recommendation

• PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation

• Doctor Specific Tag Recommendation for Online Medical Record Management

• Hierarchical Projection Enhanced Multi-behavior Recommendation

• Improving Training Stability for Multitask Ranking Models in Recommender Systems

• AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations

• SAMD: An Industrial Framework for Heterogeneous Multi-Scenario Recommendation

• TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest

• Controllable Multi-Objective Re-ranking with Policy Hypernetworks

• M5: Multi-Modal Multi-Interest Multi-Scenario Matching for Over-the-Top Recommendation

• CT4Rec: Simple yet Effective Consistency Training for Sequential Recommendation

• Multi-channel Integrated Recommendation with Exposure Constraints

• Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems

• On-device Integrated Re-ranking with Heterogeneous Behavior Modeling

• Variance Reduction Using In-Experiment Data: Efficient and Targeted Online Measurement for Sparse and Delayed Outcomes

• Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation

• VRDU: A Benchmark for Visually-rich Document Understanding

• PGLBox: Multi-GPU Graph Learning Framework for Web-Scale Recommendation

• Counterfactual Video Recommendation for Duration Debiasing

• Exploiting Intent Evolution in E-commercial Query Recommendation

• Workplace Recommendation with Temporal Network Objectives

• A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification

• Modeling Dual Period-Varying Preferences for Takeaway Recommendation

• SentiGOLD: A Large Bangla Gold Standard Multi-Domain Sentiment Analysis Dataset and its Evaluation

• Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)

• Stationary Algorithmic Balancing Over Dynamic Email Re-Ranking Problem

• Revisiting Neural Retrieval on Accelerators

• Contrastive Learning of Stress-specific Word Embedding for Social Media based Stress Detection

• Adaptive Graph Contrastive Learning for Recommendation

• BOSS: A Bilateral Occupational-Suitability-Aware Recommender System for Online Recruitment

• Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation

• PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce

• Constrained Social Community Recommendation

• Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction

• TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou

• BERT4CTR: An Efficient Framework to Combine Pre-trained Language Model with Non-textual Features for CTR Prediction

• Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach

• Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction

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