Journal of Digital Contents Society (디지털콘텐츠학회 논문지)

Digital Contents Society (한국디지털콘텐츠학회)
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Compare to Factorization Machines Learning and High-order Factorization Machines Learning for Recommend system

추천시스템에 활용되는 Matrix Factorization 중 FM과 HOFM의 비교

  • Cho, Seong-Eun (Department of Big Data, Korea University Graduate School of Computer & Information Technology)
  • 조성은 (고려대학교 컴퓨터정보통신 대학원 빅데이터 융합학과)
  • Received : 2018.04.02
  • Accepted : 2018.04.25
  • Published : 2018.04.30
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Abstract

The recommendation system is actively researched for the purpose of suggesting information that users may be interested in in many fields such as contents, online commerce, social network, advertisement system, and the like. However, there are many recommendation systems that propose based on past preference data, and it is difficult to provide users with little or no data in the past. Therefore, interest in higher-order data analysis is increasing and Matrix Factorization is attracting attention. In this paper, we study and propose a comparison and replay of the Factorization Machines Leaning(FM) model which is attracting attention in the recommendation system and High-Order Factorization Machines Learning(HOFM) which is a high - dimensional data analysis.

추천 시스템은 컨텐츠, 온라인 커머스, 소셜 네트워크, 광고 시스템 등 많은 분야에서 사용자가 관심 있을 만한 정보를 선별 제안함을 목적으로 활발하게 연구되고 있다. 그러나 과거 선호도 데이터를 기반으로 제안하는 추천시스템이 많고 과거 데이터가 적거나 없는 사용자를 대상으로는 제공하기 어려우므로 낮은 성능을 보인다는 부문에서 문제점이 있다. 따라서 더욱 고차원적인 데이터 분석에 관한 관심이 증가하고 있고 Matrix Factorization이 주목받고 있다. 이 논문은 그 중 추천시스템에서 주목받는 Factorization Machines Learning(FM)모델과 고차원 데이터 분석인 High-order Factorization Machines Learning(HOFM)의 비교와 재연을 연구하고 제안 한다.

Keywords

References

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