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Collaborative Filtering
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
From ID-based to ID-free: Rethinking ID Effectiveness in Multimodal Collaborative Filtering Recommendation
RAG-VisualRec: An Open Resource for Vision- and Text-Enhanced Retrieval-Augmented Generation in Recommendation
LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential Recommendation
CORONA: A Coarse-to-Fine Framework for Graph-based Recommendation with Large Language Models
Dual-View Disentangled Multi-Intent Learning for Enhanced Collaborative Filtering
Discrete Scale-invariant Metric Learning for Efficient Collaborative Filtering
Bridging the Gap Between Semantic and User Preference Spaces for Multi-modal Music Representation Learning
Light distillation for Incremental Graph Convolution Collaborative Filtering
Lightweight Embeddings with Graph Rewiring for Collaborative Filtering
Personalized Diffusion Model Reshapes Cold-Start Bundle Recommendation
Predictively Combatting Toxicity in Health-related Online Discussions through Machine Learning
On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective
User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation
Uncertainty in Repeated Implicit Feedback as a Measure of Reliability
SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation
Preserving Privacy and Utility in LLM-Based Product Recommendations
Graph Spectral Filtering with Chebyshev Interpolation for Recommendation
Learning Universal User Representations Leveraging Cross-domain User Intent at Snapchat
Optimal Sequential Recommendations: Exploiting User and Item Structure
Enhancing LLM-based Recommendation through Semantic-Aligned Collaborative Knowledge
Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering
Epistemic Uncertainty-aware Recommendation Systems via Bayesian Deep Ensemble Learning
Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generation
Why is Normalization Necessary for Linear Recommenders?
Can LLM-Driven Hard Negative Sampling Empower Collaborative Filtering? Findings and Potentials
Graph-based Diffusion Model for Collaborative Filtering
Squeeze and Excitation: A Weighted Graph Contrastive Learning for Collaborative Filtering
Efficient Evaluation of Large Language Models via Collaborative Filtering
Shallow AutoEncoding Recommender with Cold Start Handling via Side Features
Graphs are everywhere -- Psst! In Music Recommendation too
DrunkAgent: Stealthy Memory Corruption in LLM-Powered Recommender Agents
Improvement Graph Convolution Collaborative Filtering with Weighted addition input
CombiGCN: An effective GCN model for Recommender System
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.