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개인화된 전문가 그룹을 활용한 추천 시스템

Personalized Expert-Based Recommendation

  • 정연오 (성균관대학교 정보 및 지능시스템 연구실) ;
  • 이성우 (성균관대학교 정보 및 지능시스템 연구실) ;
  • 이지형 (성균관대학교 정보 및 지능시스템 연구실)
  • Chung, Yeounoh (Information & Intelligence System Lab., Sungkyunkwan University) ;
  • Lee, Sungwoo (Information & Intelligence System Lab., Sungkyunkwan University) ;
  • Lee, Jee-Hyong (Information & Intelligence System Lab., Sungkyunkwan University)
  • 투고 : 2012.10.28
  • 심사 : 2013.02.06
  • 발행 : 2013.02.25

초록

전문가의 지식을 기반으로 한 추천시스템에 대한 다양한 연구가 최근 활발히 진행되고 있다. 지금까지의 전문가 기반 추천 시스템이 공통된 전문가 그룹의 지식을 바탕으로 모두에게 아이템을 추천하였다면, 본 논문에서는 개인의 필요와 전문가에 대한 관점을 반영한 개인화된 전문가 그룹의 지식을 기반으로 한 추천 시스템을 제안한다. 개인화된 전문가 그룹을 찾는 과정이 제안하는 추천 시스템에서 가장 중요한 부분이다. 이를 위해 개인화된 전문가를 효율적으로 찾아내는 지지 벡터 머신(SVM) 기반 기법을 제안한다. 추천 시스템에서 널리 사용되는 k 근접이웃 알고리즘과의 비교를 통하여서 개인화된 전문가를 기반으로 한 협업 필터링 추천 시스템의 효용성을 입증한다.

Taking experts' knowledge to recommend items has shown some promising results in recommender system research. In order to improve the performance of the existing recommendation algorithms, previous researches on expert-based recommender systems have exploited the knowledge of a common expert group for all users. In this paper, we study a problem of identifying personalized experts within a user group, assuming each user needs different kinds and levels of expert help. To demonstrate this idea, we present a framework for using Support Vector Machine (SVM) to find varying expert groups for users; it is shown in an experiment that the proposed SVM approach can identify personalized experts, and that the person-alized expert-based collaborative filtering (CF) can yield better results than k-Nearest Neighbor (kNN) algorithm.

키워드

참고문헌

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