This paper introduces an improved group recommendation procedure aimed at supporting group activities in online communities. It addresses the limitations of current methods, which often prioritize the preferences of the majority, leading to dissatisfaction among minority group members. The proposed two-phase approach first generates a group recommendation set using collaborative filtering, then refines this set to better align with individual preferences, enhancing both the effectiveness of group recommendations and individual satisfaction. Experiments with a prototype system demonstrate higher precision and increased satisfaction among group members.
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Kim, Jae Kyeong, et al. "A group recommendation system for online communities." International journal of information management 30.3 (2010): 212-219.
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