Aiming at the problems of traditional point of interest (POI), such as sparse data, lack of negative feedback, and dynamic and periodic changes of user preferences, a POI recommendation method using deep learning in location-based social networks (LBSN) considering privacy protection is proposed. First, the idea of Embedding is used to quantify the user information, friend relationship, POI information, and so on, so as to obtain the internal relationship of the location. Then, based on the user's history and current POI check-in sequence set, the long- and short-term attention mechanism (LSA) is constructed, and the quantified information is used as the input of LSA to better capture the user's long-term and short-term preferences. Finally, the social network information and semantic information are fitted in different input layers, and the time and geographical location information of user's historical behavior are used to recommend the next POI for users. Gowalla and Brightkite datasets are used to demonstrate the proposed method. The results show that the performance of the proposed method is better than other comparison methods under different sparsity, location sequence length, and embedding length. When the number of iterations is 500, the recommended method tends to be stable, and the accuracy is 0.27. Moreover, the recommendation time of the proposed method is less than 130 ms, which is better than other comparative deep learning methods.

针对传统兴趣点（POI）数据稀疏、缺乏负反馈、用户偏好动态周期性变化等问题，提出一种考虑隐私的基于位置的社交网络（LBSN）深度学习的兴趣点推荐方法提出保护。首先，利用Embedding的思想对用户信息、好友关系、POI信息等进行量化，从而得到位置的内在关系。然后，基于用户的历史和当前POI签到序列集，构建长短期注意力机制（LSA），并将量化的信息作为LSA的输入，更好地捕捉用户的长期注意力和短期偏好。最后，将社交网络信息和语义信息拟合到不同的输入层，并利用用户历史行为的时间和地理位置信息为用户推荐下一个POI。Gowalla 和 Brightkite 数据集用于演示所提出的方法。结果表明，在不同的稀疏度、位置序列长度和嵌入长度下，所提方法的性能优于其他比较方法。当迭代次数为500时，推荐方法趋于稳定，准确率为0.27。而且，所提方法的推荐时间小于130 ms，优于其他对比深度学习方法。结果表明，在不同的稀疏度、位置序列长度和嵌入长度下，所提方法的性能优于其他比较方法。当迭代次数为500时，推荐方法趋于稳定，准确率为0.27。而且，所提方法的推荐时间小于130 ms，优于其他对比深度学习方法。结果表明，在不同的稀疏度、位置序列长度和嵌入长度下，所提方法的性能优于其他比较方法。当迭代次数为500时，推荐方法趋于稳定，准确率为0.27。而且，所提方法的推荐时间小于130 ms，优于其他对比深度学习方法。