The Journal of Society for e-Business Studies (한국전자거래학회지)

Society for e-Business Studies (한국전자거래학회)
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Experimental Study on Random Walk Music Recommendation Considering Users' Listening Preference Behaviors

청취 순서 성향을 고려한 랜덤워크 음악 추천 기법과 실험 사례

  • Choe, Hye-Jin (Department of Computer Science, Sookmyung Womens University) ;
  • Shim, Junho (Department of Computer Science, Sookmyung Womens University)
  • Received : 2017.06.25
  • Accepted : 2017.08.11
  • Published : 2017.08.31
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Abstract

Personalization recommendations have already proven in many areas of the e-commerce industry. For personalization recommendations, additional work such as reclassifying items is generally necessary, which requires personal information. In this study, we propose a recommendation technique that neither exploit personal information nor reclassify items. We focus on music recommendation and performed experiments with actual music listening data. Experimental analysis shows that the proposed method may result in meaningful recommendations albeit it exploits less amount of data. We analyze the appropriate number of items and present future considerations for contextual recommendation.

전자 상거래 산업에서 많이 사용되고 있는 개인화 추천은 많은 분야에서 효과를 입증하고 있다. 개인화 추천을 위해서는 개인 정보를 포함하여 아이템을 재 분류해야하는 추가 작업이 필요하다. 본 연구에서는 개인 정보를 사용하지 않고 아이템을 재분류 하지 않는 추천 기법에 대해 제안한다. 음악 추천 영역으로 제한하여 실험하였으며, 실제 청취 이력 데이터를 사용하였다. 실험 분석을 통해 적은 데이터로도 유의미한 추천을 이끌어 낼 가능성을 살피고, 상황별 추천을 위한 아이템 수 분석과 추가 기법을 제안한다.

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References

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