August 2021
Beginner to intermediate
720 pages
15h 32m
Korean
Content preview from fastai와 파이토치가 만나 꽃피운 딥러닝
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329
8
협업 필터링 깊게 알아보기
사용자가 많고 다양한 제품을 보유한 상황에서 어떤 제품이 어떤 사용자에게 가장 유용할지를
추천하는 일은 흔히 접하는 문제입니다. 영화 추천(예: 넷플릭스), 홈페이지 사용자에게 강조
할 내용 파악, 소셜 미디어 피드에 표시할 스토리 결정 등 다양한 문제로 변형해 생각할 수도
있죠. 이런 문제의 일반적인 솔루션인
collaborative
filtering
은 다음과 같이 작동합니다.
사용자
A
가 이미 구매했거나 좋아하는 제품을 확인하고, 유사 제품을 구매했거나 좋아하는 다
른 사용자
B
를 찾습니다. 그리고
B
가 구매했거나 좋아하는 제품을
A
에게 추천합니다.
예를 들어 여러분이 넷플릭스에서
1970
년대의
SF
액션 영화를 많이 시청했다고 가정해보죠.
넷플릭스는 여러분이 시청한 영화의 특징을 구체적으로는 모를 수도 있지만, 같은 영화를 본
다른 사람들도
1970
년대의
SF
액션 영화를 시청하는 경향이 있음은 알 수 있습니다. 따라서
이 접근법은 특정 영화를 누가 좋아했는지만 필요할 뿐, 영화 자체의 정보는 없어도 됩니다.
이 접근법은 사용자와 제품 간의 문제에만 국한되지 않습니다. 개념을 일반화하면 더 넓은 범
위에 적용할 수 있죠. 실제로 협업 필터링에서는
product
이라는 단어보다
item
이라는 일
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.