This document discusses neural graph collaborative filtering. It begins with an overview of recommender systems and collaborative filtering. It then discusses latent factor models, including matrix factorization and neural collaborative filtering (NCF). A key development was neural graph collaborative filtering (NGCF), which uses graph neural networks to model high-order connectivity in the user-item graph to address sparsity issues. NGCF aims to learn better embeddings by propagating user and item information through the graph.

1. Neural Graph Collaborative Filtering (2020)
Xiang Wang et al.
The 42nd International ACM SIGIR Conference on Research and Development in
Information Retrieval
Department of Industrial Engineering
Financial Engineering Lab
JunPyo Park

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Contents
Neural Graph Collaborative Filtering
Recommender System
Collaborative Filtering
Latent Factor Model
Matrix Factorization

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Contents
Neural Graph Collaborative Filtering
NCF

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Contents
Neural Graph Collaborative Filtering
NGCF

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Recommender Systems

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Recommender Systems
Goal - Increasing Product Sales
Relevance
Novelty
Serendipity
Diversity
Problem Formulation
Matrix Completion Problem
Top-k recommendation Problem

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Recommender Systems - Models
Collaborative Filtering
User-Item interaction 정보 활용 (평점, 좋아요, 장바구니, 구매내역 등…)
Content-Based
Attribute information 활용 (유저 프로필, 상품 정보 등)
Knowledge-Based
Domain Knowledge 또는 Constraint가 가미된
Demographic
Hybrid
Context-Based
Time-Sensitivity

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Recommender Systems - Models
Collaborative Filtering
User-Item interaction 정보 활용 (평점, 좋아요, 장바구니, 구매내역 등…)
Content-Based
Attribute information 활용 (유저 프로필, 상품 정보 등)
Knowledge-Based
Domain Knowledge 또는 Constraint가 가미된
Demographic
Hybrid
Context-Based
Time-Sensitivity

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Collaborative Filtering - Concepts
Collaborative Filtering models use the collaborative power of the ratings provided by
multiple users to make recommendations. The main challenge in designing collaborative
filtering methods is that the underlying ratings matrices are sparse.

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Collaborative Filtering
The basic idea of collaborative filtering methods is that these unspecified ratings can be
imputed because the observed ratings are often highly correlated across various users
and items.

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Collaborative Filtering - Methods
Memory(neighborhood)-based
User-based CF
Item-based CF
Model-based
Decision and Regression Trees
Naive bayes
Latent Factor Model
…

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Latent Factor Model
Goal is to use dimensionality reduction methods to directly estimate the data matrix in one shot.

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Latent Factor Model – Matrix Factorization (MF)

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Latent Factor Model – Matrix Factorization (MF)

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Latent Factor Model – Matrix Factorization (MF)

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Latent Factor Model – Matrix Factorization (MF)

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Latent Factor Model

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Latent Factor Model – Matrix Factorization (MF)

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Latent Factor Model – NCF

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Latent Factor Model – NCF

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Latent Factor Model – NGCF

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Latent Factor Model – NGCF

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Latent Factor Model – NGCF

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Latent Factor Model – NGCF
u1 과 비슷한 유저는?
u1 에게 추천해줄 아이템은?

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GNN Basics
From GRL textbook

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GNN Basics

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GNN Basics

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GNN Basics

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Embedding Propagation Layers

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Embedding Propagation Layers

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Embedding Propagation Layers

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NGCF Optimization

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Q&A

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Q&A
Collaborative Filtering의 문제점을 잘 지적해 주셨습니다.
특히 많은 비즈니스 분야에서 파레토 법칙(전체 결과의
80%가 전체 원인의 20%에서 일어나는 현상)이 적용 되
기 때문에
User의 과거 행동 양상에 기반한 CF는 쏠림 현상을 초래
할 수 있습니다.

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Q&A
이런 문제의 해결 방안으로는 처음에 살펴본 다른 방법론인
Content-based method 또는 Knowledge based method를 적용하
는 것 입니다.
또는 위 방법론 들을 CF와 Hybrid 하게 적용해 볼 수 있겠습니다.
Collaborative Filtering
User-Item interaction 정보 활용 (평점, 좋아요, 장바구니, 구매내역 등…)
Content-Based
Attribute information 활용 (유저 프로필, 상품 정보 등)
Knowledge-Based
Domain Knowledge 또는 Constraint가 가미된
Demographic
Hybrid
Context-Based
Time-Sensitivity

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Q&A
비즈니스 측면에서는 Serendipity(일부러 다른 취향의 아
이템을 노출)와 Diversity(추천 품목의 특성 다각화) 정도
를 조절하여 A/B 테스트 등을 통해 사용자의 만족도를 올
릴 수 있겠습니다.
Goal - Increasing Product Sales
Relevance
Novelty
Serendipity
Diversity

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Q&A

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Q&A
Pui를 통해 메시지의 감쇠가 일어나기 때
문에 멀리 있는 정보는 레이어를 거칠수
록 그 양이 줄어들게 됩니다.

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Thank you for listening!