Group recommendation confronts two major problems, i.e., unambiguous definition and identification of groups and efficient recommendation to users in groups. To tackle the two problems, a group recommendation framework based on social network community is proposed. The framework takes into account social network structural information to identify overlapping groups, which is well interpreted; and fulfills the task of recommending to groups by performing aggregation and allocation strategies using the membership of users related to groups, which considers how much users contribute to groups and benefit from groups. Experimental results on publicly open datasets demonstrate its efficiency and accuracy on the task of group recommendation.%面向用户群组的推荐主要面临如何有意义地对群组进行定义并识别，以及向群组内用户进行有效推荐两大问题。该文针对已有研究在用户群组定义解释性不强等存在的问题，提出一种基于社交网络社区的组推荐框架。该框架利用社交网络结构信息发现重叠网络社区结构作为用户群组，具有较强的可解释性，并根据用户与群组间的隶属度制定了考虑用户对群组贡献与用户从群组获利的4种聚合与分配策略，以完成组推荐任务。通过在公开数据集上与已有方法的对比实验，验证了该框架在组推荐方面的有效性和准确性。
展开▼
