그룹 추천은 개인이 아닌 그룹의 특성 및 성향을 분석하여 구성원

그룹 추천은 개인이 아닌 그룹의 특성 및 성향을 분석하여 구성원들에게 적합한 정보를 제공하는 추천 방식이다. 하지만 기존의 그룹 모델링 과정에서 사용되는 통합 전략들이 높은 선호도와 고른 분포를 갖는 관심사를 고려하지 않는 문제점이 있어, 이를 개선하고자 관심사에 대한 평균 선호도와 선호도 편차를 고려한 Average with MSD 전략을 제안한다. 제안 전략은 평균 선호도가 높으며 선호도 편차가 적은 관심사들을 추천 결과로 제공해주기 때문에 기존 그룹 모델링 전략에 비해 그룹 내 구성원 전체의 성향이 반영되며, 많은 수의 사용자가 형성하는 그룹에서도 높은 성능을 갖는다.

결과 (영어) 1:

Characteristics of the group rather than the individual and the Group recommended to analyze the tendencies of members featured to provide the appropriate information. But in the process, the existing group is used for modeling integrated strategy having high affinity and does not take into account the interests of the picked distribution issues, seeking to improve the average rating and affinity for concern considering the Average variance with MSD strategy. The proposed strategy is the average rating is high as a result of the deviation is less affinity concerns because they provide a referral to an existing group members within a group-wide inclination compared to the modeling strategy is reflected in the large number of users to deliver high performance, even at the group forming.

결과 (영어) 2:

Group recommendation is a recommendation how to analyze the characteristics and nature of the group rather than an individual provide the appropriate information to its members. But there is a problem that does not take into account the existing group integration strategy used in the modeling process are concerns with high affinity and even distribution, it is proposed to improve the Average with MSD strategy considering the average deviation preferences and preferences for concern. The proposed strategy is high and the average rating Rating deviation tendency of the entire group members compared to traditional modeling strategy reflects the group because it offers fewer concerns with the recommended result, has a high performance in a large number of users to form groups.