Location-based social networks (LBSNs) have become popular platforms that allow users to share their check-in activities with friends. Annotating semantic tags of locations, as one of the hottest research topics in LBSNs, has attracted considerable attention. Semantic annotation requires sufficient location features to train classifiers. Based on the analysis of LBSN data, we find that users’ check-in activities have similarities that can promote the extraction of location features and improve the accuracy of semantic annotation. However, the existing studies ignored the use of the similarities of users’ check-in activities for extracting suitable location features. Therefore, in this paper, a new location feature, called the similar user pattern (SUP), is first extracted by capturing the similarities among the different users’ check-in activities. Second, annotating semantic tags of locations is treated as a multi-label classification problem. Thus, multi-label semantic annotation with an extreme learning machine (ELM) is proposed, called MSA-ELM. The MSA-ELM algorithm trains a binary ELM classifier for each tag in the tag space to support multi-label classification. Finally, a series of experiments are conducted to demonstrate both the accuracy and efficiency of annotating semantic tags of locations. The experimental results show that the SUP is a proper location feature, and the MSA-ELM algorithm has a good performance for multi-label semantic annotation.

中文翻译：

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在基于位置的社交网络中注释位置的语义标签

基于位置的社交网络（LBSN）已成为流行的平台，允许用户与朋友共享其签到活动。作为LBSN中最热门的研究主题之一，对位置的语义标签进行注释已引起了广泛的关注。语义标注需要足够的位置特征来训练分类器。通过对LBSN数据的分析，我们发现用户的签到活动具有相似性，可以促进位置特征的提取并提高语义标注的准确性。但是，现有研究忽略了使用用户签到活动的相似性来提取合适的位置特征。因此，在本文中，首先通过捕获不同用户签入活动之间的相似性来提取称为相似用户模式（SUP）的新位置特征。其次，注释位置的语义标签被视为多标签分类问题。因此，提出了一种使用极限学习机（ELM）的多标签语义注释，称为MSA-ELM。MSA-ELM算法为标签空间中的每个标签训练一个二进制ELM分类器，以支持多标签分类。最后，进行了一系列实验，以说明对位置的语义标签进行注释的准确性和效率。实验结果表明，SUP是正确的定位特征，MSA-ELM算法在多标签语义标注方面具有良好的性能。MSA-ELM算法为标签空间中的每个标签训练一个二进制ELM分类器，以支持多标签分类。最后，进行了一系列实验，以说明对位置的语义标签进行注释的准确性和效率。实验结果表明，SUP是正确的定位特征，MSA-ELM算法在多标签语义标注方面具有良好的性能。MSA-ELM算法为标签空间中的每个标签训练一个二进制ELM分类器，以支持多标签分类。最后，进行了一系列实验，以说明对位置的语义标签进行注释的准确性和效率。实验结果表明，SUP是正确的定位特征，MSA-ELM算法在多标签语义标注方面具有良好的性能。