본문 바로가기

statduck

검색하기
statduck
프로필사진 statduck

  • 분류 전체보기 (35)
    • Statistics (1)
    • Machine Learning (23)
    • Bayes Stat (1)
    • Python Study (0)
    • Recommender System (2)
    • Causal Inference (0)
    • SQL (1)
    • 잡담 (2)
    • OPtimization (0)
    • 취미 & 영어공부 (2)
Guestbook
Notice
Recent Posts
Recent Comments
Link
«   2025/08   »
일 월 화 수 목 금 토
1 2
3 4 5 6 7 8 9
10 11 12 13 14 15 16
17 18 19 20 21 22 23
24 25 26 27 28 29 30
31
Tags
more
Archives
Today
Total
관리 메뉴
  • 글쓰기
  • 방명록
  • RSS
  • 관리

목록Recommender System (2)

statduck

Latent Factor Model

The basic assumption is that significant portion of the rows and columns of data matrix are highly correlated. Highly correlated data can be well explained by a low number of columns, so low-rank matrix is useful for the matrix estimation. It reduces the dimensionality by rotating the axis system, so that pairwise correlations between dimensions are removed. Low Rank Approach ✏️ $R$ has a rank $k

Recommender System 2022. 6. 3. 16:03
Neighborhood-Based Collaborative Filtering

Reference: Charu C. Aggarwal. Recommender Systems: The Textbook. Springer Neighborhood-Based method 1. Introduction Motivation This algorithm assumes that similar users show similar patterns in rating. It can be categorized into to methods. User-based collaborative filtering(cf): The predicted ratings of user A are computed from the peer group ratings. Item-based collaborative filtering(cf): The..

Recommender System 2022. 6. 3. 15:33
Prev 1 Next

Blog is powered by kakao / Designed by Tistory

티스토리툴바