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Performance Improvement of a Contents-based Recommendation System by Increasing Movie Metadata
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영화 메타데이터의 증가에 따른 콘텐츠 기반 추천 시스템 성능 향상

논문 기본 정보

Type
Proceeding
Author
Jin-kyeong Seo (평택대학교) Da-jeong Choi (평택대학교) Juryon Paik (평택대학교)
Journal
The Korean Society Of Computer And Information Proceedings of the Korean Society of Computer Information Conference Vol.30 No.1
Published
2022.1
Pages
23 - 26 (4page)

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Background
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Method
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Result
Performance Improvement of a Contents-based Recommendation System by Increasing Movie Metadata
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OTT 서비스의 이용자가 폭발적으로 증가하고 있는 지금, 사용자에게 맞춤형 상품을 추천하는 것은 해당 서비스에서 중요한 사안이다. 본 논문에서는 콘텐츠 기반 추천 시스템의 모델을 제안하고, 영화 데이터를 추가해가며 예측력을 높일 최종적인 모델을 채택하고자 한다. 이를 위해 GroupLens와 Kaggle에서 영화 데이터를 수집하고 총 1111개의 영화, 943명의 사용자에게서 나온 71026개의 영화 평가 데이터를 이용하였다. 모델평가 결과, 장르와 키워드만을 이용한 추천 시스템 모델의 RMSE는 1.3076, 단계적으로 데이터를 추가해 최종적으로 장르, 키워드, 배우, 감독, 나라, 제작사를 이용한 추천 시스템 모델의 RMSE는 1.1870으로 모든 데이터를 추가한 모델의 예측력이 더 높았다. 이에 따라 장르, 키워드, 배우, 감독, 나라, 제작사를 이용해 구현한 모델을 최종적인 모델로 채택, 무작위로 추출한 한 명의 사용자에 대한 영화 추천 리스트를 뽑아낸다.
#콘텐츠 기반 추천 시스템(contents-based recommendation system) #메타데이터(metadata) #성능 향상(performance improvement)

Contents

요약
I. Introduction
II. The Proposed Scheme
III. Conclusions
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