Skip to main content

Advertisement

Springer Nature Link
Log in

Beyond fixed time and space: next POI recommendation via multi-grained context and correlation

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

POI recommendation is significant for discovering attractive locations, crime prediction, and smart city construction. Most existing methods only consider the fixed time and space between successive check-in points when capturing sequential patterns from trajectory history. However, single granularity is inadequate to mine the spatial-temporal influence on sequential patterns in sparse and incomplete check-in data. Besides, they neglect the relevance between non-adjacent check-ins and fail to fully exploit factors for the correlation mining. To tackle the above issues, we propose a novel model for the next POI recommendation via multi-granularity context and correlation. It focuses on exploring vital factors for modeling effective spatial-temporal contexts and mining potential correlations among check-ins. Specifically, for context modeling, we explore effective spatial-temporal contexts to learn mobility patterns locally and globally by introducing hierarchical regions and slots. For correlation modeling, we only focus on the geographical influence. We employ a spatial-aware function to measure the correlations among check-ins to find the predictive ones for the recommendation. Extensive experiments on widely used datasets indicate that our MGCOCO consistently and significantly outperforms the state-of-the-art approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Medrano Rd, Aznarte JL (2021) On the inclusion of spatial information for spatio-temporal neural networks. Neural Comput Appl 33(21):14723–14740

    Article  Google Scholar 

  2. Ke S, Xie M, Zhu H, Cao Z (2022) Group-based recurrent neural network for human mobility prediction. Neural Comput Appl 1–21

  3. Do P, Pham P (2022) Heterogeneous graph convolutional network pre-training as side information for improving recommendation. Neural Comput Appl 1–17

  4. Liu H, Wang Y, Lin H, Xu B, Zhao N (2022) Mitigating sensitive data exposure with adversarial learning for fairness recommendation systems. Neural Comput Appl 1–15

  5. Ruan S, Bao J, Liang Y, Li R, He T, Meng C, Li Y, Wu Y, Zheng Y (2020) Dynamic public resource allocation based on human mobility prediction. IMWUT 1–22

  6. Chen Y, Long C, Cong G, Li C (2020) Context-aware deep model for joint mobility and time prediction. In: WSDM, 106–114

  7. Li D, Gong Z (2020) A deep neural network for crossing-city poi recommendations. TKDE 01:1–1

    MathSciNet  Google Scholar 

  8. Xu S, Fu X, Cao J, Liu B, Wang Z (2020) Survey on user location prediction based on geo-social networking data. World Wide Web 23(3):1621–1664

    Article  Google Scholar 

  9. Sun Z, Li C, Lei Y, Zhang L, Zhang J, Liang S (2021) Point-of-interest recommendation for users-businesses with uncertain check-ins. TKDE 1–1

  10. Zhang J-D, Chow C-Y, Li Y (2014) Lore: exploiting sequential influence for location recommendations. In: SIGSPATIAL 103–112

  11. Wang P, Guo J, Lan Y, Xu J, Wan S, Cheng X (2015) Learning hierarchical representation model for nextbasket recommendation. In: SIGIR 403–412

  12. He R, McAuley J (2016) Fusing similarity models with markov chains for sparse sequential recommendation. In: ICDM 191–200

  13. Guo Q, Sun Z, Zhang J, Theng Y-L (2020) An attentional recurrent neural network for personalized next location recommendation. In: AAAI 83–90

  14. Zhao K, Zhang Y, Yin H, Wang J, Zheng K, Zhou X, Xing C (2020) Discovering subsequence patterns for next poi recommendation. In: IJCAI 3216–3222

  15. Zang H, Han D, Li X, Wan Z, Wang M (2021) Cha: categorical hierarchy-based attention for next poi recommendation. TOIS 40(1):1–22

    Article  Google Scholar 

  16. Li X, Hu R, Wang Z, Yamasaki T (2021) Location predicts you: location prediction via bi-direction speculation and dual-level association. In: IJCAI 529–536

  17. Wu Y, Li K, Zhao G, Qian X (2022) Personalized long-and short-term preference learning for next poi recommendation. TKDE 34(04):1944–1957

    Google Scholar 

  18. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: NeurIPS 5998–6008

  19. Ying H, Zhuang F, Zhang F, Liu Y, Xu G, Xie X, Xiong H, Wu J (2018) Sequential recommender system based on hierarchical attention network. In :IJCAI 3926–3932

  20. Yu Z, Lian J, Mahmoody A, Liu G, Xie X (2019) Adaptive user modeling with long and short-term preferences for personalized recommendation. In: IJCAI 4213–4219

  21. Lim N, Hooi B, Ng S-K, Wang X, Goh YL, Weng R, Varadarajan J (2020) Stp-udgat: Spatial-temporal-preference user dimensional graph attention network for next poi recommendation. In: CIKM 845–854

  22. Yang D, Qu B, Yang J, Cudré-Mauroux P (2020) Lbsn2vec++: Heterogeneous hypergraph embedding for location-based social networks. TKDE 34(4):1843–1855

    Google Scholar 

  23. Luo Y, Liu Q, Liu Z (2021) Stan: spatio-temporal attention network for next location recommendation. In: WWW 2177–2185

  24. Yin H, Wang W, Wang H, Chen L, Zhou X (2017) Spatial-aware hierarchical collaborative deep learning for poi recommendation. TKDE 29(11):2537–2551

    Google Scholar 

  25. Zhong T, Zhang S, Zhou F, Zhang K, Trajcevski G, Wu J (2020) Hybrid graph convolutional networks with multi-head attention for location recommendation. World Wide Web 23(6):3125–3151

    Article  Google Scholar 

  26. Wang D, Wang X, Xiang Z, Yu D, Deng S, Xu G (2021) Attentive sequential model based on graph neural network for next poi recommendation. World Wide Web 24(6):2161–2184

    Article  Google Scholar 

  27. Cui Y, Sun H, Zhao Y, Yin H, Zheng K (2021) Sequential-knowledge-aware next poi recommendation: a meta-learning approach. TOIS 40(2):1–22

    Article  Google Scholar 

  28. Liao D, Zhong Y, Li J (2017) Location prediction through activity purpose: integrating temporal and sequential models. In: PAKDD 711–723

  29. Zhao S, Zhao T, King I, Lyu MR (2017) Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation. In: WWW 153–162

  30. Lian D, Wu Y, Ge Y, Xie X, Chen E (2020) Geography-aware sequential location recommendation. In: SIGKDD 2009–2019

  31. Wang X, Liu Y, Zhou X, Wang X, Leng Z (2022) A point-of-interest recommendation method exploiting sequential, category and geographical influence. ISPRS Int J Geo Inf 11(2):80

    Article  Google Scholar 

  32. Lian D, Zheng K, Ge Y, Cao L, Chen E, Xie X (2018) Geomf++ scalable location recommendation via joint geographical modeling and matrix factorization. TOIS 36(3):1–29

    Article  Google Scholar 

  33. Liao D, Liu W, Zhong Y, Li J, Wang G (2018) Predicting activity and location with multi-task context aware recurrent neural network. In: IJCAI 3435–3441

  34. Yu F, Cui L, Guo W, Lu X, Li Q, Lu H (2020) A category-aware deep model for successive poi recommendation on sparse check-in data. In: WWW 1264–1274

  35. Han P, Shang S, Sun A, Zhao P, Zheng K, Zhang X (2021) Point-of-interest recommendation with global and local context. TKDE 01:1–1

    Google Scholar 

  36. Liu W, Wang Z-J, Yao B, Yin J (2019) Geo-alm: poi recommendation by fusing geographical information and adversarial learning mechanism. In: IJCAI 1807–1813

  37. Liu CH, Wang Y, Piao C, Dai Z, Yuan Y, Wang G, Wu D (2020) Time-aware location prediction by convolutional area-of-interest modeling and memory-augmented attentive lstm. TKDE 34(05):2472–2484

    Google Scholar 

  38. Feng S, Tran LV, Cong G, Chen L, Li J, Li F (2020) Hme: A hyperbolic metric embedding approach for next-poi recommendation. In: SIGIR 1429–1438

  39. Han H, Zhang M, Hou M, Zhang F, Wang Z, Chen E, Wang H, Ma J, Liu Q (2020) Stgcn: a spatial-temporal aware graph learning method for poi recommendation. In: ICDM 1052–1057

  40. Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Time-aware point-of-interest recommendation In: SIGIR 363–372

  41. Du N, Dai H, Trivedi R, Upadhyay U, Gomez-Rodriguez M, Song L (2016) Recurrent marked temporal point processes: embedding event history to vector. In: SIGKDD 1555–1564

  42. Jia Y, Wang Y, Jin X, Cheng X (2016) Location prediction: a temporal-spatial bayesian model. TIST 7(3):1–25

    Article  Google Scholar 

  43. Si Y, Zhang F, Liu W (2017) Ctf-ara: an adaptive method for poi recommendation based on check-in and temporal features. Knowl Based Syst 128:59–70

    Article  Google Scholar 

  44. Yao D, Zhang C, Huang J, Bi J (2017) Serm: a recurrent model for next location prediction in semantic trajectories. In: CIKM 2411–2414

  45. Li R, Shen Y, Zhu Y (2018) Next point-of-interest recommendation with temporal and multi-level context attention. In: ICDM 1110–1115

  46. Gao Q, Zhou F, Trajcevski G, Zhang K, Zhong T, Zhang F (2019) Predicting human mobility via variational attention. In: WWW 2750–2756

  47. Sun G, Qi H, Shen Y, Yin B (2022) Tcsa-net: a temporal-context-based self-attention network for next location prediction. TITS

  48. Zhao P, Luo A, Liu Y, Zhuang F, Xu J, Li Z, Sheng VS, Zhou X (2020) Where to go next: a spatio-temporal gated network for next poi recommendation. TKDE 1–1

  49. Sun K, Qian T, Chen T, Liang Y, Nguyen QVH, Yin H (2020) Where to go next: modeling long-and short-term user preferences for point-of-interest recommendation. In: AAAI 214–221

  50. Yang D, Fankhauser B, Rosso P, Cudre-Mauroux P (2020) Location prediction over sparse user mobility traces using rnns: flashback in hidden states! In: IJCAI 2184–2190

  51. Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: SIGKDD 1082–1090

  52. Yang D, Zhang D, Zheng VW, Yu Z (2014) Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Transact Syst Man Cybern Syst 45(1):129–142

    Article  Google Scholar 

  53. Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized markov chains for next-basket recommendation. In: WWW 811–820

  54. Feng S, Li X, Zeng Y, Cong G, Chee YM, Yuan Q (2015) Personalized ranking metric embedding for next new poi recommendation. In: IJCAI 2069–2075

  55. Zhang Y, Dai H, Xu C, Feng J, Wang T, Bian J, Wang B, Liu T-Y (2014) Sequential click prediction for sponsored search with recurrent neural networks. In: AAAI 1369–1375

  56. Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. In: AAAI 194–200

  57. Feng J, Li Y, Zhang C, Sun F, Meng F, Guo A, Jin D (2018) Deepmove: predicting human mobility with attentional recurrent networks. In: WWW 1459–1468

Download references

Acknowledgements

This work was supported by the National Nature Science Foundation of China (U1803262).

Author information

Authors and Affiliations

  1. National Engineering Research Center for Multimedia Software, Wuhan University, Wuhan, China

    Xixi Li & Ruimin Hu

  2. School of Computer Science, Wuhan University, Wuhan, China

    Xixi Li & Ruimin Hu

  3. Department of Information and Communication Engineering, The University of Tokyo, Tokyo, Japan

    Zheng Wang

Authors
  1. Xixi Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ruimin Hu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Zheng Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Ruimin Hu.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Hu, R. & Wang, Z. Beyond fixed time and space: next POI recommendation via multi-grained context and correlation. Neural Comput & Applic 35, 907–920 (2023). https://doi.org/10.1007/s00521-022-07825-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07825-x

Keywords