WWW2024推荐系统论文整理，包括大模型/跨域/序列/可信推荐等热门主题

WWW2024组委会近日放出了正式接收论文清单。大会在Research Track共收到了2008篇论文，接收406篇，录用率为20.2%。本次会议将于2024年5月13日-17日在新加坡举行。完整清单见：

https://www2024.thewebconf.org/accepted/research-tracks/

近几年，推荐系统一直是WWW会议上热门主题，其中406篇接收论文中大约60多篇推荐系统相关论文，其受到了学术界和业界的广泛关注。本次我们整理了Research Track上的推荐系统相关论文。

本文整理了WWW2024上推荐系统方向的论文，共计65篇。其中主题主要涉及大模型推荐系统、推荐系统中的公平性、多模态推荐系统、推荐系统中的冷启动问题等。特别的，有较多工作进行了（大）语言模型在跨域推荐以及序列推荐方向上的尝试。由于所涉及的论文众多，大家可自行搜索寻找论文全文进行阅读。

• Intelligent Model Update Strategy for Sequential Recommendation
• A Data-Centric Multi-Objective Learning Framework for Responsible Recommendation Systems
• User Distribution Mapping Modelling with Collaborative Filtering for Cross Domain Recommendation
• Collaborative Large Language Model for Recommender Systems
• Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
• Temporal Conformity-aware Hawkes Graph Network for Recommendations
• Not All Embeddings are Created Equal: Towards Robust Cross-domain Recommendation via Contrastive Learning
• Harnessing Large Language Models for Text-Rich Sequential Recommendation
• Multi-Modal Knowledge Distillation for Recommendation with Prompt-Tuning
• Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
• Accurate Cold-start Bundle Recommendation via Popularity-based Coalescence and Curriculum Heating
• Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation
• Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
• Causally Debiased Time-aware Recommendation
• Uplift Modeling for Target User Attacks on Recommender Systems
• Physical Trajectory Inference Attack and Defense in Decentralized POI Recommendation
• Graph Contrastive Learning with Kernel Dependence Maximization for Social Recommendation
• FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval
• Top-Personalized-K Recommendation
• Debiasing Recommendation with Personal Popularity
• UnifiedSSR: A Unified Framework of Sequential Search and Recommendation
• Generative News Recommendation
• MMPOI: A Multi-Modal Content-Aware Framework for POI Recommendations
• Representation Learning with Large Language Models for Recommendation
• Challenging Low Homophily in Social Recommendation
• ReLLa: Retrieval-enhanced Large Language Models for Lifelong Sequential Behavior Comprehension in Recommendation
• Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization
• Retention Depolarization in Recommender System
• Linear-Time Graph Neural Networks for Scalable Recommendations
• Poisoning Federated Recommender Systems with Fake Users
• Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendation
• Disentangling the Long-Term Effects of Recommendations on User Consumption Patterns
• Online Billion-Scale Recommender Systems with Macro Graph Neural Networks
• Prompt-enhanced Federated Content Representation Learning for Cross-domain Recommendation
• Intersectional Two-sided Fairness in Recommendation
• Link Recommendation to Augment Influence Diffusion with Provable Guarantees
• When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions
• Recommender Transformers with Behavior Pathways
• RecDCL: Dual Contrastive Learning for Recommendation
• Ensuring User-side Fairness in Dynamic Recommender Systems
• AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
• Graph Pretraining and Prompt Learning for Recommendation
• Mirror Gradient: Towards Robust Multimodal Recommender Systems via Exploring Flat Local Minima
• Modeling Balanced Explicit and¬†Implicit Relations with Contrastive Learning for Knowledge Concept Recommendation in MOOCs
• Learning Counterfactual Explanations for Recommender Systems
• Category-based and Popularity-guided Video Game Recommendation: A Balance-oriented Framework
• Unleashing the Power of Knowledge Graph for Recommendation via Invariant Learning
• Enhancing Recommendation Accuracy and Diversity with Box Embedding: A Universal Framework
• Leave No One Behind: Online Self-Supervised Self-distillation for Sequential Recommendation
• Distributionally Robust Graph-based Recommendation System
• Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random
• Reconciling the accuracy-diversity trade-off in recommendations
• Co-clustering for Federated Recommender System
• M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation
• Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation
• Can Small Language Models be Good Reasoners in Recommender Systems?
• Negative Sampling in Next-POI Recommendations: Observation, Approach, and Evaluation
• Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation
• Federated Heterogeneous Graph Neural Network for Privacy-preserving Recommendation
• Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation
• Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training
• Hierarchical Graph Signal Processing for Collaborative Filtering
• General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout