基于LSTM的POI个性化推荐框架

近年来，随着基于位置的社会网络（Location-Based Social Network，LBSN）热度的不断增加，为用户推荐下一个POI （Point-Of-Interests）也显得越来越重要.而对应的各种应用搜集到的用户的行为时间、地理、好友以及标签等信息的增多，使得POI推荐变得更加容易.目前针对POI推荐，已经有部分算法提出，但是他们受限于自身的局限性，还都不能很好的解决这个问题，例如，个性化马尔科夫链（Factorizing Personalized Markov Chain，FPMC）、张量分解（Tensor Factorization，TF）、RNN （Recurrent Neural Networks）等.但是，这些模型由于其本身缺陷，都不能完美的糅合POI场景中的所有信息.在这篇文章中，我们扩展了长短时记忆循环神经网络（Long-ShorT Memory recurrent neural networks，LSTM），提出一种全新的推荐框架POI-LSTM来解决POI推荐问题.POI-LSTM借鉴Embedding的思想，对用户信息、好友关系、POI信息和评论信息进行向量化后，输入到神经网络中，同时利用LSTM捕捉用户的兴趣特征和兴趣的变化趋势，最终能够在不同的输入层拟合社交网络信息和语义信息，同时利用用户历史行为的时间和地理位置信息来为用户推荐下一个兴趣点.

LSTM-Based Neural Network Framework for Next POI Recommendation

As Location-Based Social Network (LBSN) services become increasingly popular, next Point-Of-Interests (POI) recommendation emerges as one of many important applications of LBSNs. With the growing ability of collecting information, more and more temporal, spatial, social contextual and semantic tags information is collected in systems, which makes the location prediction problem becomes feasible. Some works, like Factorizing Personalized Markov Chain (FPMC), Tensor Factorization (TF), Recurrent Neural Networks (RNN), etc., have been proposed to address this problem, but they all have their limitations. In this study, we extend Long-Short memory recurrent neural networks (LSTM) and propose a novel method called POI-LSTM. POI-LSTM can model social contextual and semantic tags information in each layer, and employ temporal and spatial contexts in more efficient way. Experimental results show that the proposed POI-LSTM model yields significant improvements over the competitive compared methods on two typical datasets, i.e., Yelp and Foursquare dataset.