This is a list of useful information about urban mobility prediction. Related papers, datasets and codes are included.
Papers | Authors | Code | Year | Venue | Performance |
---|---|---|---|---|---|
A recurrent model with spatial and temporal contexts.(ST-RNN)(Paper) | Qiang Liu(CAS), Shu Wu, Liang Wang, Tieniu Tan | Code | 2016 | AAAI | Gowalla: Rec@5 =0.1524, Rec@10=0.2714. GTD: Rec@5=0.4986, Rec@10=0.6812. |
Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation.(Geo-teaser)(Paper) | Shenglin Zhao(CUHK), Tong Zhao, Irwin King, and Michael R. Lyu | Code | 2017 | WWW | Foursquare: Prec@5=0.13, Prec@10=0.1, Rec@5=0.15, Rec@10=0.2 Gowalla: Prec@5=0.16, Prec@10=0.13, Rec@5=0.07, Rec@10=0.1 |
Next point-of-interest recommendation with temporal and multi-level context attention.(TMCA)(Paper) | Ranzhen Li(SJTU), Yanyan Shen, Yanmin Zhu | Code | 2018 | ICDM | Gowalla: Rec@5=0.21926, Rec@10=0.27725. Foursquare: Rec@5=0.02870, Rec@10=0.04809. |
HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction.(HST-LSTM)(Paper) | Dejiang Kong(ZJU), Fei Wu | None | 2018 | IJCAI | Baidu Map: Acc@10=0.4847, Acc@20=0.5657. |
Content-aware hierarchical point-of-interest embedding model for successive poi recommendation.(CAPE)(Paper) | Buru Chang(KU), Yonggyu Park, Donghyeon Park, Seongsoon Kim, Jaewoo Kang | Code | 2018 | IJCAI | With STELLAR: Rec@5=0.2384, Rec@10=0.2989. With LSTM: Rec@5=0.2412, Rec@10=0.3054. With GRU: Rec@5=0.2433, Rec@10=0.3079. With ST-RNN: Rec@5=0.2239, Rec@10=0.2601. |
DeepMove: Predicting Human Mobility with Attentional Recurrent Networks.(DeepMove)(Paper) | Jie Feng(THU), Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, Depeng Jin | Code | 2018 | WWW | Foursquare (NY): Rec@5=0.3372, Rec@10=0.4091. Gowalla: Rec@5=0.2021, Rec@10=0.2510. |
Long-and short-term preference learning for next poi recommendation.(LSPL)(Paper) | Yuxia Wu(XJTU), Ke Li, Guoshuai Zhao, Xueming Qian | None | 2019 | CIKM | Foursquare (NYC): Prec3@10=0.3901, Prec@20=0.4461. Foursquare (TKY): Prec@10=0.3986, Prec@20=0.4596. |
Where to go next: A spatio-temporal gated network for next poi recommendation.(STGN)(Paper19, Paper20) | Pengpeng Zhao(SUDA), Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng, Xiaofang Zhou | None | 2019 | AAAI (2020 TKDE) | Foursquare (CA): Acc@5=0.1308, Acc@10=0.1612. Foursquare (SIN): Acc@5=0.2737, Acc@10=0.3017. Gowalla: Acc@5=0.1644, Acc@10=0.2020. Brightkite: Acc@5=0.4953, Acc@10=0.5231. |
Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation.(TEMN)(Paper) | Xiao Zhou(Cambridge), Cecilia Mascolo, Zhongxiang Zhao | Code | 2019 | KDD | WeChat (GPR): TEMN (GPR): Acc@5=0.70389, Acc@10=0.81752. TEMN (CPR): Acc@5=0.72876, Acc@10=0.83398. |
Location prediction over sparse user mobility traces using rnns: Flashback in hidden states!(Flashback)(Paper) | Dingqi Yang(UM), Benjamin Fankhauser, Paolo Rosso, Philippe Cudre-Mauroux | Code | 2020 | IJCAI | Foursquare: Acc2@5=0.5399, Acc@10=0.6236. Gowalla: Acc@5=0.2754, Acc@10=0.3479. |
Discovering subsequence patterns for next poi recommendation!(ASPPA)(Paper) | Kangzhi Zhao(THU) | None | 2020 | IJCAI | Foursquare (US): Acc@10=0.3371, Acc@20=0.3950. Gowalla: Acc@10=0.2947, Acc@20=0.3573. |
Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices.(LLRec)(Paper) | Qinyong Wang(UQ) | None | 2020 | WWW | Foursquare: Acc@10=0.3542, Acc@20=0.4594. Gowalla: Acc@10=0.3874, Acc@20=0.4781. |
Personalized Long- and Short-term Preference Learning for Next POI Recommendation.(PLSPL)(Paper) | Yuxia Wu(XJTU), Ke Li, Guoshuai Zhao, Xueming Qian | None | 2020 | TKDE | Foursquare (NYC): Prec@10=0.3953, Prec@20=0.4475 Foursquare (TKY): Prec@10=0.4020, Prec@20=0.4664. |
Where to go next: Modeling long-and short-term user preferences for point-ofinterest recommendation.(LSTPM)(Paper) | Ke Sun(WHU), Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, Hongzhi Yin | Code | 2020 | AAAI | Foursquare (NY): Rec@5=0.3372, Rec@10=0.4091. Gowalla: Rec@5=0.2021, Rec@10=0.2510. |
An Interactive Multi-Task Learning Framework for Next POI Recommendation with Uncertain Check-ins.(iMTL)(Paper) | Lu Zhang( NTU) | None | 2020 | IJCAI | Foursquare (CLT): Rec@10=0.0534, Map4@10=0.0238. Foursquare (CAL): Rec@10=0.0691, Map@10=0.0443. Foursquare (PHO): Rec@10=0.0769, Map@10=0.0352. |
A Category-Aware Deep Model for Successive POI Recommendation on Sparse Check-in Data.(CatDM)(Paper) | Fuqiang Yu(SDU), Lizhen Cui, Wei Guo, Xudong Lu, Qingzhong Li, Hua Lu | Code | 2020 | WWW | Foursquare (NYC): Rec@5=0.2407, Rec@10=0.3113. Foursquare (TKY): Rec@5=0.2148, Rec@10=0.2739. |
An attentional recurrent neural network for personalized next location recommendation.(ARNN)(Paper) | Qing Guo(NTU), Zhu Sun, Jie Zhang, Yin-Leng Theng | None | 2020 | WWW | Foursquare (NY): Acc@10=0.4162, Acc@20=0.4393 Foursquare (TK): Acc@10=0.4285, Acc@20=0.4864 Gowalla (SF): Acc@10=0.2336, Acc@20=0.2530. |
Exploiting geographical-temporal awareness attention for next point-of-interest recommendation.(GT-HAN)(Paper) | Tongcun Liu(BUPT), Jianxin Liao, Zhigen Wu, Yulong Wang, Jingyu Wang | None | 2020 | Neurocomputing | Foursquare: AUC8=0.9661, acc@5: 0.13-0.15, acc@10: 0.17-0.19, acc@20: 0.23-0.25 (depending on latent dimensionality). |
Time-Aware Location Prediction by Convolutional Area-of-Interest Modeling and Memory-Augmented Attentive LSTM.(t-LocPred)(Paper) | Chi Harold Liu(BIT), Yu Wang, Chengzhe Piao, Zipeng Dai, Ye Yuan, Guoren Wang, Dapeng Wu | None | 2020 | TKDE | Gowalla: MRR5=0.247 (C=6, all), Weeplaces: MRR=0.277 (C=6, all), Brightkite: MRR=0.388 (C=4, all). |
Content-Aware Successive Point-of-Interest Recommendation.(CAPRE)(Paper) | Buru Chang(KU), Yookyung Koh, Donghyeon Park, Jaewoo Kang | None | 2020 | SDM | Foursquare: Rec@5=0.1724, Rec@10=0.2084 Instagram: Rec@5=0.2934, Rec@10=0.3588. |
Geography-Aware Sequential Location Recommendation.(GeoSAN)(Paper) | Defu Lian(USTC), Yongji Wu, Yong Ge, Xing Xie, Enhong Chen | Code | 2020 | KDD | Foursquare: Acc@5=0.3735, Acc@10=0.4867. Gowalla: Acc@5=0.4951, Acc@10=0.6028. Brightkite: Acc@5=0.5258, Acc@10=0.6425. |
Modeling hierarchical category transition for next POI recommendation with uncertain check-ins.(HCT)(Paper) | Lu Zhang(NTU), Zhu Sun, Jie Zhang, Horst Kloeden, Felix Klanner | None | 2020 | Information Sciences, Elsevier | Foursquare(SIN): Prec@5=0.613 Rec@5=0.0403 Foursquare(NYC): Prec@5=0.0585, Rec@5=0.0352 Foursquare(LA): Prec@5=0.0653, Rec@5=0.0305. |
HME: A Hyperbolic Metric Embedding Approach for Next-POI Recommendation.(HME)(Paper) | Shanshan Feng(Abu Dhabi, UAE), Lucas Vinh Tran, Gao Cong, Lisi Chen, Jing Li, Fan Li | None | 2020 | SIGIR | Foursquare (NYC): Rec@5=0.0962, Rec@10=0.1371. Foursquare (TKY): Rec@5=0.1527, Rec@10=0.2172. Gowalla (Houston): Rec@5=0.1533, Rec@10=0.2318. |
Location Prediction via Bi-direction Speculation and Dual-level Association.(paper) | Xixi Li(WHU),Ruimin Hu, Zheng Wang,Toshihiko Yamasaki | None | 2021 | ijcai | Gowalla: Acc@1=0.1454, Acc@5=0.3531, Acc@10=0.4192, MRR=0.2431. Foursquare: Acc@1=0.3068, Acc@5=0.6612, Acc@10=0.7136, MRR=0.4505. |
SNPR A Serendipity-Oriented Next POI Recommendation Model.(SNPR)(paper) | Mingwei Zhang(Northeastern University Shenyang, China),Yang Yang,Rizwan Abbas,Ke Deng,Jianxin Li,Bin Zhang | None | 2021 | CIKM | Foursquare(NewYork): Prec@3=0.05695, Prec@5=0.05325, Prec@10=0.04829, Prec@20=0.03425. Foursquare(United Kingdom): Prec@3=0.04346, Prec@5=0.03842, Prec@10=0.03651, Prec@20=0.02502. |
LightMove: A Lightweight Next-POI Recommendation forTaxicab Rooftop Advertising.(LightMove)(paper) | Jinsung Jeon(Yonsei University,Seoul, South Korea),Soyoung Kang,Minju Jo, Seunghyeon Cho,Noseong Park,Seonghoon Kim,Chiyoung Song | code | 2021 | CIKM | Texi: Hits@1=0.9988, Hits@5=1.0000, Hits@10=1.0000, MRR=0.9994. Foursquare: Hits@1=0.1545, Hits@5=0.3203, Hits@10=0.3656, MRR=0.2288. LA: Hits@1=0.3209, Hits@5=0.4431, Hits@10=0.4758, MRR=0.0.3756. |
ST-PIL: Spatial-Temporal Periodic Interest Learning for Next Point-of-Interest Recommendation.(ST-PIL) (paper) | Qiang Cui(Meituan, Beijing, China), Chenrui Zhang, Yafeng Zhang, Jinpeng Wang, Mingchen Cai | None | 2021 | CIKM | NYC: Acc@1=0.3807, Acc@5=0.5850, Acc@10=0.6454, MRR@5=0.4584, MRR@10=0.4666. TKY: Acc@1=0.3523, Acc@5=0.5963, Acc@10=0.6772, MRR@5=0.4455, MRR@10=0.4564. |
STAN: Spatio-Temporal Attention Network for Next Location Recommendation.(STAN)(paper) | Yingtao Luo(University of Washington),Qiang Liu,Zhaocheng Liu | code | 2021 | www | Gowalla: Recall@5 =0.3016, Recall@10=0.3998. TKY: Recall@5 =0.3461, Recall@10=0.4264. SIN: Recall@5 =0.3751, Recall@10=0.4301. NYC: Recall@5 =0.4669, Recall@10=0.5962. |