Journal of KIISE:Databases (한국정보과학회논문지:데이타베이스)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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PReAmacy: A Personalized Recommendation Algorithm considering Contents and Intimacy between Users in Social Network Services

PReAmacy: 소셜 네트워크 서비스에서 콘텐츠와 사용자의 친밀도를 고려한 개인화 추천 알고리즘

  • 서영덕 (고려대학교 컴퓨터 전파통신공학과) ;
  • 김정동 (고려대학교 컴퓨터 전파통신공학과) ;
  • 백두권 (고려대학교 융합소프트웨어전문대학원)
  • Received : 2013.12.16
  • Accepted : 2014.04.16
  • Published : 2014.08.15
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Abstract

Various characteristics of social network contents such as real-time, people relationship and big data can help to improve personalized recommender systems. Among them, 'people relationship' is a key factor of recommendation, so many personalized recommender systems utilizing it have been proposed. However, existing researches can not reflect personal tendency and are unable to provide precise recommendations in various domains, because they do not consider intimacy among people. In this paper, to solve these problems, we propose PReAmacy, a Personalized Recommendation Algorithm, considering intimacy among users and various characteristics of social network contents. Our experimental results indicate that not only the precision of PReAmacy is higher than that of existing algorithms, but intimacy is of great importance in PReAmacy.

'실시간성', '사람들 간의 관계정보', '빅 데이터'와 같은 다양한 특성을 갖는 소셜 네트워크 콘텐츠는 개인화 추천 시스템의 성능 향상에 큰 도움이 되고 있다. 그 중 '사람들 간의 관계정보'가 가장 중요한 역할을 하기 때문에, 이를 활용한 다양한 연구가 진행되고 있다. 하지만 기존의 연구에서는 사람들간의 친밀도를 고려하지 않고 있어서 개인의 성향을 반영하기 어렵고 다양한 도메인에서 정확한 추천이 불가능하다. 본 논문은 기존 연구의 문제를 해결하기 위해 사용자간 친밀도를 측정하는 친밀도 알고리즘과 소셜 네트워크의 다양한 특성에 기반한 개인화 추천 알고리즘인 PReAmacy를 제안한다. 실험을 통해 PReAmacy가 기존의 알고리즘에 비해 높은 성능을 가지며 친밀도가 PReAmacy 알고리즘에 큰 비중을 차지한다는 것을 보였다.

Keywords

Acknowledgement

Supported by : 한국연구재단

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