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Recommendation System Study Day4 - 6 (19.02) 본문

------- DS -------/RS

Recommendation System Study Day4 - 6 (19.02)

enent 2022. 2. 20. 16:34
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4)Day4_Matrix Factorization 

 

Matrix Factorization

Recommendation System_Day4

yeo0.github.io

 

- YouTube

 

www.youtube.com

 

- YouTube

 

www.youtube.com

 

5)Day5_현대 세대의 Recommender System 장단점에 대한 이해 

 

현대 세대의 Recommender System의 장단점에 대한 이해

Recommendation System_Day5

yeo0.github.io

 

Introduction to Recommender Systems

Recommender systems aim to predict users' interests and recommend product items that quite likely are interesting for them. They are among the most powerful machine learning systems that e-commerce companies implement in order to drive sales.

tryolabs.com

 

온라인 "필터 버블"을 주의하세요

웹 기업들이 그들의 서비스(뉴스와 검색 결과를 포함하여)를 우리의 개인적 성향에 맞추기 위해 노력할 때, 위험하고 의도하지 않은 결과가 나타납니다. 우리는 "필터 버블"의 함정에 빠지고, 우

www.ted.com

 

6)Day6_Cosine Similarity와 Pearson Correlation을 사용한 User based 및 Item based nearest neighbor Collaborative Filtering에 대한 이해

 

Cosine similarity 와 Pearson correlation을 사용한 User based 및 Item based nearest neighbor Collaborative Filtering에 대

Recommendation System_Day6

yeo0.github.io

 

Recommender Systems (Machine Learning Summer School 2014 @ CMU)

Slides for my 4 hour tutorial on Recommender Systems at the 2014 Machine Learning School at CMU

www.slideshare.net

 

Similarity Functions for User-User Collaborative Filtering

Typically, user-user collaborative filtering has used Pearson correlation to compare users. Early work tried Spearman correlation and (raw) cosine similarity, but found Pearson to work better, and …

grouplens.org

 

 

 

 

 

 

 

 

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