A new point-of-interest group recommendation method in location-based social networks
A new point-of-interest group recommendation method in location-based social networks
复制标题
基于位置的社交网络中新的兴趣点群体推荐方法
DOI：
10.1007/s00521-020-04979-4
复制
发表时间：
2020
影响因子：
6
通讯作者：
Sun Yongjiao
中科院分区：
文献类型：
--
作者：
Zhao Xiangguo;Zhang Zhen;Bi Xin;Sun Yongjiao
兴趣点群推荐是基于位置的社交网络中的一个研究热点，它为一组用户推荐最适合的地点。然而，传统的兴趣点群推荐方法只生成一个共识函数，将个体偏好聚合成群体偏好，没有考虑所有影响兴趣点群推荐结果的因素，导致推荐准确率较低。更重要的是，这些方法有很长的运行时间。因此，在本文中，我们提出了一种新的POI群推荐方法与极端学习机（ELM）称为PGR-ELM。PGR-ELM方法将兴趣点群组推荐视为一个二元分类问题。首先，从兴趣点流行度、群体成员与兴趣点的距离、群体成员兴趣偏好结合群体成员间的亲密度三个因素中提取三个特征。这些特征同时考虑了所有影响推荐结果的因素，保证了兴趣点群推荐的有效性。然后，提取的特征输入到训练ELM分类器，因为它的快速学习速度，这保证了兴趣点群推荐的效率。最后，大量的实验验证了PGR-ELM方法的准确性和效率。
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.