Personal Information
Organization / Workplace
Korea Korea, South
Occupation
software engineer at kakao
Website
brunch.co.kr/@goodvc78
About
My identity is RecSys knowledge, Sense for data analysis, Fastest learning curve, Enjoy my jobs
The fully experience of Recsys in live service.
Tags
추천시스템
데이터분석
recommender system
#music recsys
추천시스템목표
개인화경험
mab
t-sne
feature visualization
r
#feature visualization
데이터야놀자
탐색적데이터분석
data analytics
eda
컨벤션
네이밍
quantify-self
파이썬분석
자아정량화
recsys
word2vec
recommendersystem similarity
personal analytics
추천아 놀자
k-means
kmeans
cosine similarity
추천
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Presentations
(15)Likes
(138)Engagement, Metrics & Personalisation at Scale
Mounia Lalmas-Roelleke
•
3 years ago
LinkedIn talk at Netflix ML Platform meetup Sep 2019
Faisal Siddiqi
•
4 years ago
Digital 2020 Global Digital Overview (January 2020) v01
DataReportal
•
4 years ago
아이템 추천의 다양성을 높이기 위한 후처리 방법(논문 리뷰)
hyunsung lee
•
6 years ago
Data council SF 2020 Building a Personalized Messaging System at Netflix
Grace T. Huang
•
3 years ago
데이터를 얻으려는 노오오력
Youngjae Kim
•
6 years ago
Recent Trends in Personalization at Netflix
Justin Basilico
•
3 years ago
Lessons learned from building practical deep learning systems
Xavier Amatriain
•
4 years ago
SXSW2017 @NewDutchMedia Talk: Exploration is the New Search
Lora Aroyo
•
7 years ago
Tutorial on Online User Engagement: Metrics and Optimization
Mounia Lalmas-Roelleke
•
4 years ago
RecSys 2020 A Human Perspective on Algorithmic Similarity Schendel 9-2020
Zachary Schendel
•
3 years ago
Beyond DAUs and MAUs, 3 Key Levers to Understanding User Engagement For Your Mobile Apps
CleverTap
•
4 years ago
Facebook prophet
Minho Lee
•
6 years ago
Is Growth Important? Yes. But Retention Is King
TheFamily
•
9 years ago
The Holy Grail of Traction - Brian Balfour, HubSpot
Traction Conf
•
8 years ago
Measuring user engagement: the do, the do not do, and the we do not know
Mounia Lalmas-Roelleke
•
9 years ago
Personalizing the listening experience
Mounia Lalmas-Roelleke
•
4 years ago
A/B 테스트를 적용하기 어려울 때, 이벤트 효과 추정하기 (2020-01-18 잔디콘)
Minho Lee
•
4 years ago
Engagement, metrics and "recommenders"
Mounia Lalmas-Roelleke
•
4 years ago
Calibrated Recommendations
Harald Steck
•
5 years ago
Search-Based Serving Architecture of Embeddings-Based Recommendations (RecSys 2019)
Sonya Liberman
•
4 years ago
Recent Trends in Personalization: A Netflix Perspective
Justin Basilico
•
4 years ago
A Multi-Armed Bandit Framework For Recommendations at Netflix
Jaya Kawale
•
5 years ago
2019 cvpr paper_overview
LEE HOSEONG
•
5 years ago
Word2Vec Network Structure Explained
Subhashis Hazarika
•
5 years ago
Visualizing Data Using t-SNE
David Khosid
•
8 years ago
Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence
Dawen Liang
•
7 years ago
Adaptation and Evaluation of Recommendationsfor Short-term Shopping Goals
LukasLerche
•
8 years ago
Steffen Rendle, Research Scientist, Google at MLconf SF
MLconf
•
9 years ago
딥러닝 기본 원리의 이해
Hee Won Park
•
6 years ago
Personal Information
Organization / Workplace
Korea Korea, South
Occupation
software engineer at kakao
Website
brunch.co.kr/@goodvc78
About
My identity is RecSys knowledge, Sense for data analysis, Fastest learning curve, Enjoy my jobs
The fully experience of Recsys in live service.
Tags
추천시스템
데이터분석
recommender system
#music recsys
추천시스템목표
개인화경험
mab
t-sne
feature visualization
r
#feature visualization
데이터야놀자
탐색적데이터분석
data analytics
eda
컨벤션
네이밍
quantify-self
파이썬분석
자아정량화
recsys
word2vec
recommendersystem similarity
personal analytics
추천아 놀자
k-means
kmeans
cosine similarity
추천
See more