推荐系统是数据挖掘的关键技术。近年来，随着基于位置的社会网络的蓬勃发展，以用户签到内容为核心的时空大数据不断涌现，其高维、异质等特征，对传统推荐方法提出了严峻的挑战。本项目尝试采用深度学习来解决兴趣点推荐问题。已有研究表明，深度神经网络非线性结构可以获得比传统推荐方法更好的结果，但无法应对兴趣点推荐中数据稀疏性及用户偏好动态性两大挑战性问题。有鉴于此，本项目将聚焦于稀疏数据及动态偏好的深度学习兴趣点推荐方法研究，具体包括:1)建立基于深度神经网络的兴趣点推荐模型，并研究基于隐式社交关系、多模数据和生成对抗网络的模型扩展方法；2)建立基于循环神经网络的下一个兴趣点推荐模型，并研究基于长短期偏好、注意力机制和联合学习的模型扩展方法；3)面向兴趣点交互推荐场景，研究基于深度神经网络记忆增强的Bandit方法；4)研发兴趣点推荐系统原型。本项目将为位置感知时代的时空数据推荐理论和实践提供有益参考。

The recommendation is one of the core techniques in data mining. With the dramatic growth of location-based social networks (LBSNs) in most recent years, the spatial-temporal data of user check-ins is with a huge volume, high dimensionality, and heterogeneity which severely challenges traditional recommendation systems. Our project aims to deal with the challenges in point of interest (POI) recommendation utilizing deep learning. Some existing studies have shown the advantages of deep learning over many traditional recommendation methods. Our project will focus on two challenges (data sparsity and dynamic preferences of users) in point-of-interest (POI) recommendation, and develop novel deep learning methods for POI recommendation with dynamic preferences on sparse data and. Specifically, 1) we will design a recommendation framework for POI recommendation based on the deep neural network, and propose recommendation methods for POI recommendation based on implicit social relationships, multimode data and generative adversarial nets (GANs); 2) we will build next POI recommendation models based on recurrent neural networks, and propose recommendation methods based on long-short-term preferences, attention mechanisms and joint learning; 3) we will propose a deep neural memory augmented bandit method for POI interactive recommendation; 4) we will finally develop a prototypical recommendation system and integrate our methods inside the system. Briefly, the outcomes of this project will provide great values to both theories and practices of spatial-temporal data recommendation in the location-aware era.