문서 보기역링크PDF로 내보내기맨 위로 이 문서는 읽기 전용입니다. 원본을 볼 수는 있지만 바꿀 수는 없습니다. 문제가 있다고 생각하면 관리자에게 문의하세요. # Basic Recommender Systems {{tag>Basic Recommender Systems}} ## Learning Objectives 1. Basic Concepts - Distinguish the different input data of a Recommender System - Classify the different families of Recommender System - Distinguish between implicit and explicit user feedback - Express the Global Effects formul 2. Requirements and Evaluation - Decide the most appropriate evaluation metric for the recommendation task - Decide the most appropriate splitting strategy - Distinguish between error and accuracy metrics - Discuss the importance of beyond accuracy metrics 3. Content-Based filtering - Define a content-based recommender system - Describe how to improve the different similarity functions - Discuss the importance of weighting the attributes - Describe advantages and disadvantages of Content-Based Filtering vs Collaborative Filtering 4. Collaborative Filtering - Select the most appropriate similarity function - Describe the difference between item-based and user-based models - Describe association rules based recommenders - Decide the most appropriate approach based on implicit or explicit feedback ## Basic Concepts ![](https://i.imgur.com/AFwBL1K.jpg) ### Taxonomy of Recommender Systems - Algorithems - Non-Personalized - Personalized - Content-Based Filtering ([[CBF]]) - Collaborative Filtering ([[CF]]) - User Based - Item Based - Matrix Factorization - Others (with CARS, Hybrids) - Factorization Machine - Deep Learning - Context-Aware ([[CARS]]) - Hybrids Side Information ![](https://i.imgur.com/efLbS9x.jpg) ### Item Content Matrix ## 출처 - https://www.coursera.org/learn/basic-recommender-systems/home/welcome open/basic-recommender-systems.txt 마지막으로 수정됨: 2020/11/03 13:08저자 127.0.0.1