The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Evaluation of Collaborative Filtering Methods for Developing Online Music Contents Recommendation System

온라인 음악 콘텐츠 추천 시스템 구현을 위한 협업 필터링 기법들의 비교 평가

  • Yoo, Youngseok (Division of Human IT SW Convergence, Daejin University) ;
  • Kim, Jiyeon (Division of Human IT SW Convergence, Daejin University) ;
  • Sohn, Bangyong (Division of Human IT SW Convergence, Daejin University) ;
  • Jung, Jongjin (Division of Human IT SW Convergence, Daejin University)
  • Received : 2017.05.30
  • Accepted : 2017.06.23
  • Published : 2017.07.01
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Abstract

As big data technologies have been developed and massive data have exploded from users through various channels, CEO of global IT enterprise mentioned core importance of data in next generation business. Therefore various machine learning technologies have been necessary to apply data driven services but especially recommendation has been core technique in viewpoint of directly providing summarized information or exact choice of items to users in information flooding environment. Recently evolved recommendation techniques have been proposed by many researchers and most of service companies with big data tried to apply refined recommendation method on their online business. For example, Amazon used item to item collaborative filtering method on its sales distribution platform. In this paper, we develop a commercial web service for suggesting music contents and implement three representative collaborative filtering methods on the service. We also produce recommendation lists with three methods based on real world sample data and evaluate the usefulness of them by comparison among the produced result. This study is meaningful in terms of suggesting the right direction and practicality when companies and developers want to develop web services by applying big data based recommendation techniques in practical environment.

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