Papers with Code 아카이브
Recommendation Systems
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 11
- 평가표
- 55
- 보존된 평가 기록
- 251
Recommendation System in AI Research
A Recommendation System is a specialized AI-driven model that analyzes user preferences and behaviors to suggest relevant content, products, or services. It is widely used in domains like e-commerce, streaming platforms, social media, and personalized learning.
AI research in recommendation systems focuses on:
- Collaborative Filtering: Predicting user preferences based on similar users' choices.
- Content-Based Filtering: Recommending items based on user history and item characteristics.
- Hybrid Models: Combining multiple techniques for better accuracy.
- Deep Learning & Transformers: Using neural networks and self-attention mechanisms for personalized recommendations.
- Graph-Based Approaches: Leveraging knowledge graphs for relationship-aware recommendations.
Key challenges include data sparsity, scalability, and bias mitigation. Cutting-edge research explores reinforcement learning, explainability, and privacy-preserving methods to enhance recommendation systems.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose
Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation
IP2: Entity-Guided Interest Probing for Personalized News Recommendation
A Reproducibility Study of Product-side Fairness in Bundle Recommendation
Multi-Channel Graph Neural Network for Financial Risk Prediction of NEEQ Enterprises
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
Similarity-Guided Diffusion for Contrastive Sequential Recommendation
Looking for Fairness in Recommender Systems
Catching Bid-rigging Cartels with Graph Attention Neural Networks
Journalism-Guided Agentic In-Context Learning for News Stance Detection
LLM-Stackelberg Games: Conjectural Reasoning Equilibria and Their Applications to Spearphishing
When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in Recommendation
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
Credit Risk Analysis for SMEs Using Graph Neural Networks in Supply Chain
Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming
GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models
GNN-ViTCap: GNN-Enhanced Multiple Instance Learning with Vision Transformers for Whole Slide Image Classification and Captioning
Boosting Parameter Efficiency in LLM-Based Recommendation through Sophisticated Pruning
A Language-Driven Framework for Improving Personalized Recommendations: Merging LLMs with Traditional Algorithms
Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study
Graph Learning
Kamae: Bridging Spark and Keras for Seamless ML Preprocessing
Hierarchical Interaction Summarization and Contrastive Prompting for Explainable Recommendations
Unconditional Diffusion for Generative Sequential Recommendation
Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation
LumiCRS: Asymmetric Contrastive Prototype Learning for Long-Tail Conversational Movie Recommendation
An analysis of vision-language models for fabric retrieval
Interest Networks (iNETs) for Cities: Cross-Platform Insights and Urban Behavior Explanations
High Order Collaboration-Oriented Federated Graph Neural Network for Accurate QoS Prediction
BiFair: A Fairness-aware Training Framework for LLM-enhanced Recommender Systems via Bi-level Optimization
Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs
RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes
DARTS: A Dual-View Attack Framework for Targeted Manipulation in Federated Sequential Recommendation
The Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.