/Recommender_System

Based on user behavior(implicit) or explicit feedback, Recommend the better appropriate items to each user

Primary LanguageJupyter Notebook

Kaist Internship Program

Own Logo

DSAIL

Laborartory

  • Data Science and Artificial Intelligence Laboratory DSAIL

Duration

2020-12-28~202.2.19

Subject

Recommender System, Graph Embedding
image

Objective

Construct the Recommender Systems Using Graph Embedding Methodology

Process

Recommendation System

Term(2021) Paper Implementation
01-04 Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model SVD, SVD++
- MATRIX FACTORIZATION TECHNIQUES FOR RECOMMENDER SYSTEMS KNN
- Probabilistic Matrix Factorization
01-08 Collaborative Filtering for Implicit Feedback Datasets
- BPR: Bayesian Personalized Ranking from Implicit Feedback BPR
01-15 Collaborative Metric Learning CML, PPT Content
- Neural Collaborative Filtering
- AutoRec: Autoencoders Meet Collaborative Filtering
- Collaborative Deep Learning for Recommender Systems
01-22 Factorization Machines FM
- Wide & Deep Learning for Recommender Systems Wide&Deep
- SoRec: Social Recommendation Using Probabilistic Matrix Factorization Sorec
- Recommender Systems with Social Regularization

Graph

Term(2021) Paper Implementation
01-29 Deep Neural Networks for Youtube Recommendation
- DeepWalk:Online Learning of Social Representations
- node2vec:Scalable Feature Learning for Networks Node2Vec
- Semi-Supervised Classification With Graph Convolutional Networks GCN
- Graph Attention Networks
02-05 Large Scale Information Network Embedding LINE
- metapath2vec: Scalable Representation Learning for Heterogeneous Networks
02-15 Deep Graph Informax DGI
- Inductive Representation Learning on Large Graphs
- Auto-Encoding Variational Bayes
- Variational Graph Auto-Encoders

Library Or Useful Resource