2.按照主要技术划分
2.1 GNN-based
- Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation 【多任务图循环网络】
- An Attribute-Driven Mirroring Graph Network for Session-based Recommendation 【特征驱动的反射图网络】
- Co-clustering Interactions via Attentive Hypergraph Neural Network 【超图神经网络聚类交互】
- Graph Trend Filtering Networks for Recommendation 【图趋势过滤网络】
- EFLEC: Efficient Feature-LEakage Correction in GNN based Recommendation Systems 【short paper，高效的特征泄露修正】
- DH-HGCN: Dual Homogeneity Hypergraph Convolutional Network for Multiple Social Recommendations 【short paper，双同质超图卷积网络】
- Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation【short paper，意图解耦增强超图神经网络】
- DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation 【short paper， 需求感知的图神经网络】
2.2 RL-based
- Locality-Sensitive State-Guided Experience Replay Optimization for Sparse-Reward in Online Recommendation 【在线推荐中的稀疏奖励问题】
- Multi-Agent RL-based Information Selection Model for Sequential Recommendation 【多智能体信息选择】
- Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective 【从提示视角看用于推荐的强化学习】
- Doubly-Adaptive Reinforcement Learning for Cross-Domain Interactive Recommendation 【双重适应的强化学习】
- MGPolicy: Meta Graph Enhanced Off-policy Learning for Recommendations 【元图增强的离线策略学习】
- Value Penalized Q-Learning for Recommender Systems 【short paper，值惩罚的Q-Learning】
- Revisiting Interactive Recommender System with Reinforcement Learning 【short paper，回顾基于强化学习的交互推荐】
2.3 Contrastive Learning based
- A Review-aware Graph Contrastive Learning Framework for Recommendation 【考虑评论的图对比学习】
- Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation 【简单的图对比学习方法】
- Knowledge Graph Contrastive Learning for Recommendation 【知识图谱上的对比学习】
- Self-Augmented Recommendation with Hypergraph Contrastive Collaborative Filtering 【超图上的对比学习】
- Multi-level Cross-view Contrastive Learning for Knowledge-aware Recommender System 【多级交叉视图的对比学习】
- Dual Contrastive Network for Sequential Recommendation 【short paper，双对比网络】
- Improving Micro-video Recommendation via Contrastive Multiple Interests 【short paper，对比多兴趣提升短视频推荐】
- An MLP-based Algorithm for Efficient Contrastive Graph Recommendations 【short paper，基于MLP的算法实现高效图对比】
- Multi-modal Graph Contrastive Learning for Micro-video Recommendation 【short paper，多模态图对比学习】
- Towards Results-level Proportionality for Multi-objective Recommender Systems 【short paper，动量对比方法】
- Socially-aware Dual Contrastive Learning for Cold-Start Recommendation 【short paper，社交感知的双重对比学习】
2.4 AutoML-based Recommender System
- Single-shot Embedding Dimension Search in Recommender System 【嵌入维度搜索】
- AutoLossGen: Automatic Loss Function Generation for Recommender Systems 【自动损失函数生成】
- NAS-CTR: Efficient Neural Architecture Search for Click-Through Rate Prediction 【高效的网络结构搜索】
2.5 Others
- Forest-based Deep Recommender 【深度森林】
- Deployable and Continuable Meta-Learning-Based Recommender System with Fast User-Incremental Updates 【基于元学习的可部署可拓展推荐系统】
