two real-world datasets. The results demonstrate that our STA model achieves the state-of-the-art performance in terms of high recommendation accuracy, robustness to data sparsity and effectiveness in handling cold start problem.'
author:
- |
    Bei Liu[$~^{1}$]{}, Tieyun Qian[$~^{1}$]{}, Bing Liu[$~^{2}$]{}, Liang Hong[$~^{3}$]{}, Zhenni You[$~^{1}$]{}, Yuxiang Li[$~^{1}$]{}\
    [$~^{1}$]{} State Key Laboratory of Software Engineering, Wuhan University, China\
    [$~^{2}$]{} Department of Computer Science, University of Illinois at Chicago, USA\
    [$~^{3}$]{} School of Information Management, Wuhan University, China\
    {qty, beiliu}@whu.edu.cn, liub@cs.uic.edu, {hong, znyou, liyux}@whu.edu.cn\
title: 'Learning Spatiotemporal-Aware Representation for POI Recommendation'
---

Introduction
============

Location-based social networks (LBSN), such as Foursquare, Yelp, and Facebook Places, are becoming pervasive in our daily lives. Users on LBSN like to share their experiences with their friends for points of interest (POIs), e.g., restaurants and museums. The providers of location-based services have collected a huge amount of users’ check-in data, which facilitates the recommendation of POIs to unvisited users. The POI recommendation is of high value to both the users and companies, and thus has attracted much attention from researchers in recent years [@Cheng_tist16; @Zhu_kdd15; @Chen_aaai15; @Gao_aaai15].

Most existing studies mainly focused on leveraging spatial information due to the well-known strong correlation between users’ activities and geographical 