Personalized place semantics recognition is the process of giving individual semantic labels to locations, e.g., “home” and “school”. Capturing personalized place semantics exactly is critical for location-based services. To address the problems of existing methods, i.e., the insufficient utilization of context information and the neglect of the semantic correlation across related tasks, we propose a method for personalized place semantics recognition, which employs embedding methods, including deep learning based embedding and word embedding, to obtain effective representations from multi-context information (e.g., system settings, phone usage, and user activities). Meanwhile, we jointly model personalized place semantics and App usage sequences by sharing the App representations, which can improve generalization capability by exploiting the commonalities and differences across related tasks. We evaluate the proposed method on the Mobile Data Challenge dataset, and experimental results show that it outperforms existing methods significantly.

个性化地点语义识别是将各个语义标签赋予位置（例如“家”和“学校”）的过程。准确地捕获个性化位置语义对于基于位置的服务至关重要。为了解决现有方法存在的问题，即上下文信息的利用不足以及相关任务之间语义相关性的忽视，我们提出了一种个性化的场所语义识别方法，该方法采用了基于深度学习的嵌入和词嵌入的嵌入方法，以从多上下文信息（例如，系统设置，电话使用情况和用户活动）中获得有效的表示。同时，我们通过共享App表示，共同对个性化的地点语义和App使用顺序进行建模，通过利用相关任务之间的共性和差异，可以提高泛化能力。我们在Mobile Data Challenge数据集上评估了该方法，实验结果表明该方法明显优于现有方法。