[Coursera] Introduction to Recommender Systems W1

https://www.coursera.org/learn/recommender-systems/home/welcome

Introduction to Recommender Systems

by University of Minnesota

추천 시스템 강의는 총 8주의 강으로 구성되어져 있다.

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1Week : Introduction to Recommender Systems

2Week : Non-Personalized Recommenders

3Week : Content-Based Recommenders

4Week : User-User Collaborative Filtering

5Week : Evaluation

6Week : Item Based

7Week : Dimensionality Reduction

8Week : Advanced Topics

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이번주 내용은 

==== Introduction to Recommender Systems ====

늘 그렇듯 간단한 강의 설명과 추천 시스템이 무엇인지 소개하는 내용이었다.

강의 내용중 기억하고 싶은 내용들이다. 

추천 시스템의 분류 (Taxonomy of Recommender Systems)

Analytical Framework

분석 요소 

- Domain

- Purpose

- Recommendation Context

- Whose Opinions

- Personalization Level

- Privacy and Trustworthiness

- Interfaces

- Recommendation Algorithms

 

Recommendation Algorithms

- Non-Personalized Summary Statistics

- Content-Based Filtering

- Collaborative Filtering

- Other (Critique, Interview Based Recommendations)

Linking these together 

User (User Model | User Attributes)

Item (Item Attributes)

Ratings

각 상황에 따라 셋의 요소 분석 및 관계 파악