Building Similarity Based Recommendation System

4.5
별점
18개의 평가
제공자:
Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

Understand what is collaborative filtering and how to collect data to build a recommendation system

Understand how to create user item interactions matrix to find which users are most similar to the other users

Build a similarity based recommendation system based on collaborative filtering

Clock2 hour
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

Welcome to this 1-hour project-based course on Building Similarity Based Recommendation System. In this project, you will learn how similarity based collaborative filtering recommendation systems work, how you can collect data for building such systems. You will learn what are some different ways you to compute similarity between users and recommend items based on products interacted by other similar users. You will learn to create user item interactions matrix from the original dataset and also how to recommend items to a new user who does not have any historical interactions with the items. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

개발할 기술

Data Manipulationcosine similarityRecommender Systems

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. Understanding collaborative filtering and dataset

  2. Exploring the dataset

  3. Creating user item interactions matrix

  4. Finding similar users

  5. Creating similarity based recommendation system

  6. Conclusion

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

자주 묻는 질문

자주 묻는 질문

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