Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

Xi SUN; Zhimin LV

doi:10.1631/FITEE.2200304

Your Location：

Home >

Browse articles >

Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation

Regular Papers | Updated：2023-09-21

- Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation
- 一种基于非线性时空效应的个性化下一个兴趣点推荐方法
- Frontiers of Information Technology & Electronic Engineering Vol. 24, Issue 9, Pages: 1273-1286(2023)
- Affiliations：
  
  Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
- Author bio：
  
  E-mail: b20190537@xs.ustb.edu.cn
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2200304
  CLC： TP181
- Received：15 July 2022，
  
  Revised：2023-02-23，
  
  Accepted：23 February 2023，
  
  Published：0 September 2023
- Accepted：
Scan QR Code
Xi SUN, Zhimin LV. Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(9): 1273-1286.
DOI：

Xi SUN, Zhimin LV. Exploring nonlinear spatiotemporal effects for personalized next point-of-interest recommendation[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(9): 1273-1286. DOI： 10.1631/FITEE.2200304.

摘要

下一个兴趣点（POI）推荐是基于位置的社交网络（LBSN）的一项重要任务，其目标是使用历史签到数据在特定情境下为用户推荐下一个兴趣点。现有研究将用户时空信息线性离散化，然后使用基于循环神经网络（RNN）的方法进行建模。但是这些研究忽略了时空信息对用户偏好的非线性影响以及用户轨迹和候选兴趣点之间的时空相关性。为解决这些问题，本文提出一种时空轨迹（STT）模型。该模型使用具有注意力机制的长短期记忆网络（LSTM）作为基本框架，并将时空信息以编码形式引入模型。在编码信息过程中，使用指数型衰减因子刻画用户兴趣随时间和距离的非线性漂移特性。此外，本文在目标召回过程中设计一个时空匹配模块，该模块通过测量用户历史轨迹与候选集之间的相关性来为用户筛选最有可能的下一个兴趣点。本文使用4个真实数据集评估STT模型性能。实验结果表明，本文所提方法的推荐效果比主流的推荐模型有显著提升。

Abstract

Next point-of-interest (POI) recommendation is an important personalized task in location-based social networks (LBSNs) and aims to recommend the next POI for users in a specific situation with historical check-in data. State-of-the-art studies linearly discretize the user’s spatiotemporal information and then use recurrent neural network (RNN) based models for modeling. However

these studies ignore the nonlinear effects of spatiotemporal information on user preferences and spatiotemporal correlations between user trajectories and candidate POIs. To address these limitations

a spatiotemporal trajectory (STT) model is proposed in this paper. We use the long short-term memory (LSTM) model with an attention mechanism as the basic framework and introduce the user’s spatiotemporal information into the model in encoding. In the process of encoding information

an exponential decay factor is applied to reflect the nonlinear drift of user interest over time and distance. In addition

we design a spatiotemporal matching module in the process of recalling the target to select the most relevant POI by measuring the relevance between the user’s current trajectory and the candidate set. We evaluate the performance of our STT model with four real-world datasets. Experimental results show that our model outperforms existing state-of-the-art methods.

关键词

Keywords

references

Abdollahi B , Nasraoui O , 2016 . Explainable restricted Boltzmann machines for collaborative filtering . https://doi.org/10.48550/arXiv.1606.07129 https://doi.org/10.48550/arXiv.1606.07129

Aggarwal CC , Han JW , Wang JY , et al. , 2004 . A framework for projected clustering of high dimensional data streams . In: Nascimento MA , Özsu MT , Kossmann D , et al . (Eds.) , Proc VLDB Conf Elsevier , Amsterdam , p. 852 - 863 . https://doi.org/10.1016/B978-012088469-8.50075-9 https://doi.org/10.1016/B978-012088469-8.50075-9

Chen KS , Xu ZH , Gong LY , 2012 . Research of the distance calculation algorithm based on RSSI . Adv Mater Res , 499 : 464 - 468 . https://doi.org/10.4028/www.scientific.net/AMR.499.464 https://doi.org/10.4028/www.scientific.net/AMR.499.464

Cheng HT , Koc L , Harmsen J , et al. , 2016 . Wide & deep learning for recommender systems . Proc 1 st Workshop on Deep Learning for Recommender Systems , p. 7 - 10 . https://doi.org/10.1145/2988450.2988454 https://doi.org/10.1145/2988450.2988454

Cui Q , Tang YY , Wu S , et al. , 2019 . Distance2Pre: personalized spatial preference for next point-of-interest prediction . 23 rd Pacific-Asia Conf on Advances in Knowledge Discovery and Data Mining , p. 289 - 301 . https://doi.org/10.1007/978-3-030-16142-2_23 https://doi.org/10.1007/978-3-030-16142-2_23

Fan W , 2004 . Systematic data selection to mine concept-drifting data streams . Proc 10 th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining , p. 128 - 137 . https://doi.org/10.1145/1014052.1014069 https://doi.org/10.1145/1014052.1014069

Feng J , Li Y , Zhang C , et al. , 2018 . DeepMove: predicting human mobility with attentional recurrent networks . Proc World Wide Web Conf , p. 1459 - 1468 . https://doi.org/10.1145/3178876.3186058 https://doi.org/10.1145/3178876.3186058

Feng SS , Li XT , Zeng YF , et al. , 2015 . Personalized ranking metric embedding for next new POI recommendation . Proc 24 th Int Conf on Artificial Intelligence , p. 2069 - 2075 .

Gao HJ , Tang JL , Hu X , et al , 2013 . Exploring temporal effects for location recommendation on location-based social networks . Proc 7 th ACM Conf on Recommender Systems , p. 93 - 100 . https://doi.org/10.1145/2507157.2507182 https://doi.org/10.1145/2507157.2507182

Gehring J , Auli M , Grangier D , et al. , 2017 . Convolutional sequence to sequence learning . 34 th Int Conf on Machine Learning , p. 1243 - 1252 .

Guo Q , Sun Z , Zhang J , et al. , 2020 . An attentional recurrent neural network for personalized next location recommendation . Proc 34 th AAAI Conf on Artificial Intelligence , p. 83 - 90 . https://doi.org/10.1609/aaai.v34i01.5337 https://doi.org/10.1609/aaai.v34i01.5337

He XN , Liao LZ , Zhang HW , et al. , 2017 . Neural collaborative filtering . Proc 26 th Int Conf on World Wide Web , p.‍ 173 - 182 . https://doi.org/10.1145/3038912.3052569 https://doi.org/10.1145/3038912.3052569

Hochreiter S , Schmidhuber J , 1997 . Long short-term memory . Neur Comput , 9 ( 8 ): 1735 - 1780 . https://doi.org/10.1162/neco.1997.9.8.1735 https://doi.org/10.1162/neco.1997.9.8.1735

Jiang SH , Qian XM , Shen JL , et al. , 2015 . Author topic model-based collaborative filtering for personalized POI recommendations . IEEE Trans Multim , 17 ( 6 ): 907 - 918 . https://doi.org/10.1109/TMM.2015.2417506 https://doi.org/10.1109/TMM.2015.2417506

Kang WC , McAuley J , 2018 . Self-attentive sequential recommendation . IEEE Int Conf on Data Mining , p. 197 - 206 . https://doi.org/10.1109/ICDM.2018.00035 https://doi.org/10.1109/ICDM.2018.00035

Kingma DP , Ba J , 2014 . Adam: a method for stochastic optimization . https://doi.org/10.48550/arXiv.1412.6980 https://doi.org/10.48550/arXiv.1412.6980

Li JC , Wang YJ , McAuley J , 2020 . Time interval aware self-attention for sequential recommendation . Proc 13 th Int Conf on Web Search and Data Mining , p. 322 - 330 . https://doi.org/10.1145/3336191.3371786 https://doi.org/10.1145/3336191.3371786

Lian DF , Zheng VW , Xie X , 2013 . Collaborative filtering meets next check-in location prediction . Proc 22 nd Int Conf on World Wide Web , p. 231 - 232 . https://doi.org/10.1145/2487788.2487907 https://doi.org/10.1145/2487788.2487907

Lian DF , Wu YJ , Ge Y , et al. , 2020 . Geography-aware sequential location recommendation . Proc 26 th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining , p.‍ 2009 - 2019 . https://doi.org/10.1145/3394486.3403252 https://doi.org/10.1145/3394486.3403252

Liu Q , Wu S , Wang DY , et al. , 2016a . Context-aware sequential recommendation . IEEE 16 th Int Conf on Data Mining , p. 1053 - 1058 . https://doi.org/10.1109/ICDM.2016.0135 https://doi.org/10.1109/ICDM.2016.0135

Liu Q , Wu S , Wang L , et al. , 2016b . Predicting the next location: a recurrent model with spatial and temporal contexts . Proc 30 th AAAI Conf on Artificial Intelligence , p. 194 - 200 . https://doi.org/10.1609/aaai.v30i1.9971 https://doi.org/10.1609/aaai.v30i1.9971

Liu YC , Liu CR , Liu B , et al. , 2016 . Unified point-of-interest recommendation with temporal interval assessment . Proc 22 nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining , p. 1015 - 1024 . https://doi.org/10.1145/2939672.2939773 https://doi.org/10.1145/2939672.2939773

Luo YT , Liu Q , Liu ZC , 2021 . STAN: spatio-temporal attention network for next location recommendation . Proc Web Conf , p. 2177 - 2185 . https://doi.org/10.1145/3442381.3449998 https://doi.org/10.1145/3442381.3449998

Mikolov T , Kombrink S , et al. , 2011 . Extensions of recurrent neural network language model . IEEE Int Conf Acoustics Speech and Signal Processing , p. 5528 - 5531 . https://doi.org/10.1109/ICASSP.2011.5947611 https://doi.org/10.1109/ICASSP.2011.5947611

Qian TY , Liu B , Nguyen QVH , et al. , 2019 . Spatiotemporal representation learning for translation-based POI recommendation . ACM Trans Inform Syst , 37 ( 2 ): 1 - 24 . https://doi.org/10.1145/3295499 https://doi.org/10.1145/3295499

Ren Z , Fang F , et al. , 2022 . State of the art in defect detection based on machine vision . Int J Precis Eng Manuf-Green Techn , 9 ( 2 ): 661 - 691 . https://doi.org/10.1007/s40684-021-00343-6 https://doi.org/10.1007/s40684-021-00343-6

Rendle S , Freudenthaler C , Schmidt-Thieme L , 2010 . Factorizing personalized Markov chains for next-basket recommendation . Proc 19 th Int Conf on World Wide Web , p. 811 - 820 . https://doi.org/10.1145/1772690.1772773 https://doi.org/10.1145/1772690.1772773

Salakhutdinov R , Mnih A , 2007 . Probabilistic matrix factorization . Proc 20 th Int Conf on Neural Information Processing Systems , p. 1257 - 1264 .

Si YL , Zhang FZ , Liu WY , 2019 . An adaptive point-of-interest recommendation method for location-based social networks based on user activity and spatial features . Knowl-Based Syst , 163 : 267 - 282 . https://doi.org/10.1016/j.knosys.2018.08.031 https://doi.org/10.1016/j.knosys.2018.08.031

Sun K , Qian TY , Chen T , et al. , 2020 . Where to go next: modeling long- and short-term user preferences for point-of-interest recommendation . Proc 34 th AAAI Conf on Artificial Intelligence , p. 214 - 221 . https://doi.org/10.1609/aaai.v34i01.5353 https://doi.org/10.1609/aaai.v34i01.5353

Wang HX , Fan W , Yu PS , et al. , 2003 . Mining concept-drifting data streams using ensemble classifiers . Proc 9 th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining , p. 226 - 235 . https://doi.org/10.1145/956750.956778 https://doi.org/10.1145/956750.956778

Wu YX , Li K , Zhao GS , et al. , 2022 . Personalized long- and short-term preference learning for next POI recommendation . IEEE Trans Knowl Data Eng , 34 ( 4 ): 1944 - 1957 . https://doi.org/10.1109/TKDE.2020.3002531 https://doi.org/10.1109/TKDE.2020.3002531

Xu CH , Ding AS , Zhao KD , 2021 . A novel POI recommendation method based on trust relationship and spatial‍‒‍temporal factors . Electr Commer Res Appl , 48 : 101060 . https://doi.org/10.1016/j.elerap.2021.101060 https://doi.org/10.1016/j.elerap.2021.101060

Xu S , Cao JX , Legg P , et al. , 2020 . Venue2Vec: an efficient embedding model for fine-grained user location prediction in geo-social networks . IEEE Syst J , 14 ( 2 ): 1740 - 1751 . https://doi.org/10.1109/JSYST.2019.2913080 https://doi.org/10.1109/JSYST.2019.2913080

Yang C , Bai LX , Zhang C , et al. , 2017 . Bridging collaborative filtering and semi-supervised learning: a neural approach for POI recommendation . Proc 23 rd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining , p. 1245 - 1254 . https://doi.org/10.1145/3097983.3098094 https://doi.org/10.1145/3097983.3098094

Yang DQ , Zhang DQ , Zheng VW , et al. , 2015 . Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs . IEEE Trans Syst Man Cybern Syst , 45 ( 1 ): 129 - 142 . https://doi.org/10.1109/TSMC.2014.2327053 https://doi.org/10.1109/TSMC.2014.2327053

Yang DQ , Fankhauser B , Rosso P , et al. , 2020 . Location prediction over sparse user mobility traces using RNNs: flashback in hidden states! Proc 29 th Int Joint Conf on Artificial Intelligence , p. 2184 - 2190 . https://doi.org/10.24963/ijcai.2020/302 https://doi.org/10.24963/ijcai.2020/302

Ye M , Yin PF , Lee WC , 2010 . Location recommendation for location-based social networks . Proc 18 th SIGSPATIAL Int Conf on Advances in Geographic Information Systems , p. 458 - 461 . https://doi.org/10.1145/1869790.1869861 https://doi.org/10.1145/1869790.1869861

Yin HZ , Wang WQ , Wang H , et al. , 2017 . Spatial-aware hierarchical collaborative deep learning for POI recommendation . IEEE Trans Knowl Data Eng , 29 ( 11 ): 2537 - 2551 . https://doi.org/10.1109/TKDE.2017.2741484 https://doi.org/10.1109/TKDE.2017.2741484

Ying HC , Zhuang FZ , Zhang FZ , et al. , 2018 . Sequential recommender system based on hierarchical attention network . Proc 27 th Int Joint Conf on Artificial Intelligence , p. 3926 - 3932 . https://doi.org/10.24963/ijcai.2018/546 https://doi.org/10.24963/ijcai.2018/546

Yuan Q , Cong G , Ma ZY , et al. , 2013 . Time-aware point-of-interest recommendation . Proc 36 th Int ACM SIGIR Conf on Research and Development in Information Retrieval , p. 363 - 372 . https://doi.org/10.1145/2484028.2484030 https://doi.org/10.1145/2484028.2484030

Yuan Q , Cong G , Sun AX , 2014 . Graph-based point-of-interest recommendation with geographical and temporal influences . Proc 23 rd ACM Int Conf on Information and Knowledge Management , p. 659 - 668 . https://doi.org/10.1145/2661829.2661983 https://doi.org/10.1145/2661829.2661983

Zeng J , Tang HR , Zhao YZ , et al. , 2021 . PR-RCUC: a POI recommendation model using region-based collaborative filtering and user-based mobile context . Mobile Netw Appl , 26 ( 6 ): 2434 - 2444 . https://doi.org/10.1007/s11036-021-01782-w https://doi.org/10.1007/s11036-021-01782-w

Zhang T , Zheng WM , Cui Z , et al. , 2019 . Spatial–temporal recurrent neural network for emotion recognition . IEEE Trans Cybern , 49 ( 3 ): 839 - 847 . https://doi.org/10.1109/TCYB.2017.2788081 https://doi.org/10.1109/TCYB.2017.2788081

Zhao GS , Lou PL , Qian XM , et al. , 2020 . Personalized location recommendation by fusing sentimental and spatial context . Knowl-Based Syst , 196 : 105849 . https://doi.org/10.1016/j.knosys.2020.105849 https://doi.org/10.1016/j.knosys.2020.105849

Zhao KZ , Zhang Y , Yin HZ , et al. , 2020 . Discovering subsequence patterns for next POI recommendation . Proc 29 th Int Joint Conf on Artificial Intelligence , p. 3216 - 3222 . https://doi.org/10.24963/ijcai.2020/445 https://doi.org/10.24963/ijcai.2020/445

Zhao PP , Luo AJ , Liu YC , et al. , 2022 . Where to go next: a spatio-temporal gated network for next POI recommendation . IEEE Trans Knowl Data Eng , 34 ( 5 ): 2512 - 2524 . https://doi.org/10.1109/TKDE.2020.3007194 https://doi.org/10.1109/TKDE.2020.3007194

Zhao SL , Zhao T , Yang HQ , et al. , 2016 . STELLAR: spatial-temporal latent ranking for successive point-of-interest recommendation . Proc 30 th AAAI Conf on Artificial Intelligence , p. 315 - 322 . https://doi.org/10.1609/aaai.v30i1.9986 https://doi.org/10.1609/aaai.v30i1.9986

Zhao WX , Zhou NN , Sun AX , et al. , 2018 . A time-aware trajectory embedding model for next-location recommendation . Knowl Inform Syst , 56 ( 3 ): 559 - 579 . https://doi.org/10.1007/s10115-017-1107-4 https://doi.org/10.1007/s10115-017-1107-4

Views

330

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Attention-based efficient robot grasp detection network

LDformer: a parallel neural network model for long-term power forecasting

A personality-guided affective brain–computer interface for implementation of emotional intelligence in machines

ECGID: a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model

Related Author

Xuedian ZHANG

Songwen PEI

Changxiang HE

Chen XIAO

Xiaofei QIN

Wenkai HU

Ran TIAN

Xinmei LI

Related Institution

Shanghai Institute of Intelligent Science and Technology, Tongji University

Key Laboratory of Biomedical Optical Technology and Devices of Ministry of Education

Shanghai Key Laboratory of Modern Optical System

College of Science, University of Shanghai for Science and Technology

School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰