RecSys

Collaborative Filtering Recommendation

KAIST GSDS 대학원 박찬영 교수님의 수업인 추천시스템 및 그래프 기계학습 수업을 필기입니다. Collaborative Filtering(CF) Approach Use the "wisdom of the crowd" to recommend items Basic assumption and idea Users give ratings to items Customers who had similar tastes in the past, will have similar tastes in the future(입맛 안변해) Memory(Neighborhood) based - CF Main idea Similar users display similar patterns of rating behavior -> User-b..

Content-Based Recommendation

KAIST GSDS 대학원 박찬영 교수님의 수업인 추천시스템 및 그래프 기계학습 수업 필기입니다. Goal: Recommend items similar to those the user liked When is it useful? -> Useful when ratings of other users are not available How to defines similarity? Profile is vector of features and calculate computing similarity How to pick important features? -> TF-IDF (Term frequency X Inverse Doc Frequency) Term frequency-Inverse Doc Frequency(TF..

Introduction to Recommender System

KAIST GSDS 대학원 박찬영 교수님의 수업인 추천시스템 및 그래프 기계학습 수업을 필기입니다. Goal of Recommender system To identify things the we might like To help people discover new content To discover which things go together To personalize user experiences in response to user feedback History of Recommender system 1990s - First systems(e.g., GroupLens), basic algorithms 2000 ~ 2005 - Research explosion, mainstream applications..