The task of next POI recommendations has been studied extensively in recent years. However, developing a unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging, because of the heterogeneity nature of these information. Further, effective mechanisms to smoothly handle cold-start cases are also a difficult topic. Inspired by the recent success of neural networks in many areas, in this paper, we propose a simple yet effective neural network framework, named NEXT, for next POI recommendations. NEXT is a unified framework to learn the hidden intent regarding user’s next move, by incorporating different factors in a unified manner. Specifically, in NEXT, we incorporate meta-data information, e.g., user friendship and textual descriptions of POIs, and two kinds of temporal contexts (i.e., time interval and visit time). To leverage sequential relations and geographical influence, we propose to adopt DeepWalk, a network representation learning technique, to encode such knowledge. We evaluate the effectiveness of NEXT against other state-of-the-art alternatives and neural networks based solutions. Experimental results on three publicly available datasets demonstrate that NEXT significantly outperforms baselines in real-time next POI recommendations. Further experiments show inherent ability of NEXT in handling cold-start.

中文翻译：

---

NEXT：用于下一个POI推荐的神经网络框架

下一个POI建议的任务近年来已被广泛研究。但是，由于这些信息的异质性，开发一个统一的建议框架以合并与POI和用户相关联的多个因素仍然具有挑战性。此外，有效处理冷启动情况的有效机制也是一个困难的话题。受到神经网络在许多领域的最新成功的启发，在本文中，我们提出了一个简单而有效的神经网络框架，名为NEXT，用于下一个POI建议。NEXT是一个统一的框架，它通过以统一的方式结合不同的因素来学习有关用户下一步行动的隐藏意图。具体而言，在NEXT中，我们合并了元数据信息，例如用户友好性和POI的文本描述，以及两种时间上下文（即时间间隔和访问时间）。为了利用顺序关系和地理影响力，我们建议采用网络表示学习技术DeepWalk对此类知识进行编码。我们将NEXT与其他最新技术和基于神经网络的解决方案进行比较，评估其有效性。在三个可公开获得的数据集上的实验结果表明，NEXT在实时的下一个POI建议中明显优于基线。进一步的实验表明，NEXT具有处理冷启动的固有能力。在三个可公开获得的数据集上的实验结果表明，NEXT在实时的下一个POI建议中明显优于基线。进一步的实验表明，NEXT具有处理冷启动的固有能力。在三个可公开获得的数据集上的实验结果表明，NEXT在实时的下一个POI建议中明显优于基线。进一步的实验表明，NEXT具有处理冷启动的固有能力。