In mobile social networks, next point-of-interest (POI) recommendation is a very important function that can provide personalized location-based services for mobile users. In this paper, we propose a recurrent neural network (RNN)-based next POI recommendation approach that considers both the location interests of similar users and contextual information (such as time, current location, and friends’ preferences). We develop a spatial-temporal topic model to describe users’ location interest, based on which we form comprehensive feature representations of user interests and contextual information. We propose a supervised RNN learning prediction model for next POI recommendation. Experiments based on real-world dataset verify the accuracy and efficiency of the proposed approach, and achieve best F 1-score of 0.196 754 on the Gowalla dataset and 0.354 592 on the Brightkite dataset.

中文翻译：

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下一个基于位置兴趣挖掘和循环神经网络的 POI 推荐

在移动社交网络中，下一个兴趣点（POI）推荐是一个非常重要的功能，可以为移动用户提供个性化的基于位置的服务。在本文中，我们提出了一种基于循环神经网络 (RNN) 的下一个 POI 推荐方法，该方法同时考虑了相似用户的位置兴趣和上下文信息（例如时间、当前位置和朋友的偏好）。我们开发了一个时空主题模型来描述用户的位置兴趣，在此基础上我们形成了用户兴趣和上下文信息的综合特征表示。我们为下一个 POI 推荐提出了一个有监督的 RNN 学习预测模型。基于真实世界数据集的实验验证了所提出方法的准确性和效率，并在 Gowalla 数据集上实现了 0.196 754 的最佳 F 1-score 和 0。