POI group recommendation is one of the hottest research topics in location-based social networks, which recommends the most agreeable places for a group of users. However, traditional POI group recommendation methods only generate a consensus function to aggregate individual preference into group preference and they do not consider all the factors that can determine the results of POI group recommendation, which leads to a low recommendation accuracy. What’s more, these methods have a long running time. Therefore, in this paper, we propose a new POI group recommendation method with an extreme learning machine (ELM) called PGR-ELM. The PGR-ELM method regards POI group recommendation as a binary classification problem. First, three features are extracted from three factors: POI popularity, group members’ distance to POI, members’ interest preferences combined affinity between group members. These features simultaneously consider all the factors that can determine the results of recommendation and guarantee the effectiveness of POI group recommendation. Then, the extracted features are input to train an ELM classifier because of its fast learning speed, which guarantees the efficiency of POI group recommendation. Finally, extensive experiments verify the accuracy and efficiency of PGR-ELM method.

POI小组推荐是基于位置的社交网络中最热门的研究主题之一，它为一组用户推荐最适合的地点。但是，传统的POI小组推荐方法仅生成共识功能以将个人偏好汇总为小组偏好，并且没有考虑所有可以确定POI小组推荐结果的因素，因此推荐准确性较低。而且，这些方法的运行时间很长。因此，在本文中，我们提出了一种新的带有极端学习机（ELM）的POI组推荐方法，称为PGR-ELM。PGR-ELM方法将POI组推荐视为二进制分类问题。首先，从三个因素中提取三个特征：兴趣点受欢迎程度，小组成员与兴趣点的距离，成员的兴趣偏好结合了组成员之间的亲和力。这些功能同时考虑了可以确定推荐结果并保证POI小组推荐有效性的所有因素。然后，由于其学习速度快，因此提取的特征被输入以训练ELM分类器，从而保证了POI组推荐的效率。最后，大量实验验证了PGR-ELM方法的准确性和有效性。