The Journal of Korean Institute of Information Technology (한국정보기술학회논문지)

Korean Institute of Information Technology (한국정보기술학회)
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An Artificial Neural Network for Local Library's Book Recommender System

지역 도서관을 위한 도서 추천 인공 신경망 모델

  • 최혜봉 (한동대학교 ICT창업학부)
  • Received : 2016.08.25
  • Accepted : 2016.09.20
  • Published : 2016.09.30
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Abstract

For the last decade, recommender system has served as one of most successful business applications in data science. Many recommender systems focus on online commercial service such as e-commerce and online streaming service where massive online activities are accumulated as Big Data. On the other hand, small offline-based services suffer from data sparsity and insufficient data volume to train a complex recommender model such as artificial neural network which leads the system to over-fitting problem. In this paper, we propose an elaborate recommender system that addresses the issue of data sparsity and insufficient data volume of local library using collaborative filtering method. It combines the result with user and book profile to train a complex neural network model to predict user preference on un-read book. Moreover we prove that the system is well-suited to the local library environment with comprehensive empirical study on real library data.

지난 십여 년간 추천 시스템은 데이터 과학의 가장 성공적인 비즈니스 응용사례로 소개되어져 왔다. 많은 경우 추천 시스템은 사용자들의 이용 정보가 빅데이터로 축적되는 온라인 서비스를 중심으로 발달되고 있다. 상대적으로 오프라인 기반의 서비스들은 데이터의 희소성 및 데이터의 양이 충분하지 않은 문제가 나타난다. 따라서 신경망 모델과 같이 복잡도가 높은 모델들을 추천 시스템에 사용하는 경우 과적합 문제가 발생한다. 본 논문에서는 지역 도서관 서비스를 위한 인공 신경망 기반 도서 추천 시스템을 제안한다. 협업 필터링을 사용하여 데이터의 희소성 및 부족한 데이터양을 보완하여 도서 추천을 위한 신경망 모델을 학습한다. 학습한 신경망 모델을 이용하여 사용자의 읽지 않은 도서들에 대한 선호도를 예측하고 도서를 추천한다. 실제 도서관의 대출 데이터를 사용해 제안하는 도서 추천 시스템이 소규모 도서관 환경에서 효과적으로 동작함을 보인다.

Keywords

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