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추천시스템

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AI
CS
Major

공부

Netflix Challenge

•
Netflix Challenge를 통해 Collaborative Filtering을 구현 → RMSE 1.01 달성
•
이후 논문들을 읽고 pytorch를 공부하기 시작하면서 다양한 추천 시스템 구현 방식을 공부하였고 Matrix Factorization(SVD), Factorization Machine, GNN 방식으로 구현하여 실험
◦
최종적으로 RMSE 0.89 달성

연구 미팅

Recommender System with LLM

A Survey on Large Language Models for Recommendation.pdf
1745.4KB
Representation Learning with Large Language Models for Recommendation.pdf
3121.0KB

Recommendation System Basics

CML.pdf
1303.3KB
DeepFM with FM,Wide&Deep.pdf
1273.4KB
Probabilstic Matrix Factorization.pdf
469.4KB
SASRec.pdf
807.8KB

Sequential RecSys 주제 탐색

Diversity,Time series.pdf
902.3KB
Sequential Rec.pdf
606.3KB

Sequential RecSys 관련 논문 탐색

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Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation
20240930-Research meeting.pptx
1189.9KB
•
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
20241006-Research meeting.pptx
1794.4KB
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Sequential Recommendation with Context-Aware Collaborative Graph Attention Networks
20241030-Research meeting.pptx
3163.3KB
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Time node embedding
20241108-Research meeting.pptx
1050.1KB
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Revisit Collaborative Signal in Sequential RecSys & SelfGNN
20241120-Research meeting.pptx
1002.7KB
•
Experiment & Consideration
20241204-Research meeting.pptx
1128.6KB
Table
Search
sequential recommendation 논문들
이름
발행 연도
순위
읽었나?
읽을건가?
풀려는 문제
학회
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation
Open
0
0
❌
❌
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
Open
2021
0
✅
✅
global-context
CIKM
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
Open
0
0
✅
✅
Transition
Dual Disentangled Attention for Multi-Information Utilization in Sequential Recommendation
Open
0
0
Graph+Time
Contrastive Learning for Sequential Recommendation
Open
0
0
✅
Transition
noise
Graph-Based Embedding Smoothing for Sequential Recommendation
Open
0
0
Graph+Time
HTP: Exploiting Holistic Temporal Patterns for Sequential Recommendation
Open
0
0
Graph+Time
Temporal Interest Network for User Response Prediction
Open
0
0
Graph+Time
Graph-coupled time interval network for sequential recommendation
Open
0
1
✅
global-context
Multi-temporal Sequential Recommendation Model Based on the Fused Learning Preferences
Open
0
0
✅
Graph+Time
Towards evolving software recommendation with time-sliced social and behavioral information
Open
0
0
Graph+Time
Continuous-Time Sequential Recommendation with Transformer and Graph Neural Network
Open
0
0
Graph+Time
Time Interval Aware Self-Attention for Sequential Recommendation
Open
0
0
✅
Graph+Time
Hybrid Graph Neural Network Recommendation Based on Multi-Behavior Interaction and Time Sequence Awareness
Open
0
0
✅
Graph+Time
Node representation learning with graph augmentation for sequential recommendation
Open
0
1
✅
global-context
TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems
Open
0
0
✅
global-context
Graph+Time
Dynamic time-aware collaborative sequential recommendation with attention-based network
Open
0
0
🔺
global-context
Multi-dimensional Graph Neural Network for Sequential Recommendation
Open
2023
1
✅
global-context
Graph+Time
Ranking on user–item heterogeneous graph for Ecommerce next basket recommendations
Open
0
2
✅
Graph+Time
Fusing collaborative transformation with temporally aware target interaction networks for sequential recommendation
Open
0
0
✅
global-context
Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation.pdf
Open
0
0
✅
✅
Tripartite
Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation
Open
2021
0
✅
✅
global-context
CIKM
Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation
Open
0
0
Dynamic Change
Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation
Open
0
0
❌
etc
Session-based Recommendation with Graph Neural Networks
Open
0
0
✅
✅
global-context
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