Group recommendation has become highly demanded when users communicate in the forms of group activities in online sharing communities. These group activities include student group study, family TV program watching, friends travel decision, etc. Existing group recommendation techniques mainly focus on the small user groups. However, online sharing communities have enabled group activities among thousands of users. Accordingly, recommendation over large groups has become urgent. In this paper, we propose a new framework to accomplish this goal by exploring the group interests and the connections between group users. We first divide a big group into different interest subgroups, each of which contains users closely connected with each other and sharing the similar interests. Then, for each interest subgroup, our framework exploits the connections between group users to collect a comparably compact potential candidate set of media-user pairs, on which the collaborative filtering is performed to generate an interest subgroup-based recommendation list. After that, a novel aggregation function is proposed to integrate the recommended media lists of all interest subgroups as the final group recommendation results. Extensive experiments have been conducted on two real social media datasets to demonstrate the effectiveness and efficiency of our proposed approach.

中文翻译：

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基于动态连接的社交群组推荐

当用户在在线共享社区中以群组活动的形式进行交流时，群组推荐变得非常需要。这些小组活动包括学生小组学习、家庭电视节目观看、朋友出行决策等。现有的小组推荐技术主要针对小用户群体。然而，在线共享社区已经实现了数千用户之间的群组活动。因此，对大群体的推荐变得紧迫。在本文中，我们提出了一个新框架，通过探索群组兴趣和群组用户之间的联系来实现这一目标。我们首先将一个大组划分为不同的兴趣子组，每个子组包含彼此紧密联系并共享相似兴趣的用户。然后，对于每个兴趣子组，我们的框架利用组用户之间的联系来收集相对紧凑的媒体用户对的潜在候选集，在其上执行协同过滤以生成基于兴趣子组的推荐列表。之后，提出了一种新的聚合函数，将所有兴趣子组的推荐媒体列表整合为最终的组推荐结果。已经在两个真实的社交媒体数据集上进行了大量实验，以证明我们提出的方法的有效性和效率。提出了一种新的聚合函数，将所有兴趣子组的推荐媒体列表整合为最终的组推荐结果。已经在两个真实的社交媒体数据集上进行了大量实验，以证明我们提出的方法的有效性和效率。提出了一种新的聚合函数，将所有兴趣子组的推荐媒体列表整合为最终的组推荐结果。已经在两个真实的社交媒体数据集上进行了大量实验，以证明我们提出的方法的有效性和效率。