Skip to main content
SpringerLink
Log in

Exploiting multi-attention network with contextual influence for point-of-interest recommendation

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Point-of-Interest (POI) recommendation has become an important service on Location-Based Social Networks (LBSNs). In order to improve the performance of recommendation, besides the check-in data generated in LBSNs, researchers are striving to exploit various auxiliary information such as social relation among users and geographical influence among neighbourhood POIs. However, existing works cannot effectively study the diverse degrees of influence from user’s friends, neither are they able to capture the feature impacts of POIs in the preference modelling process. To overcome these challenges, by making use of a M ulti-A ttention N etwork to learn the C ontextual influence of both users and POIs, this paper presents a model named MANC for POI recommendation. The MANC model consists of two parts: a user-friend module and a POI neighbourhood module. Unlike existing works which treat the influences from different friends of a user equally, the user-friend module in MANC applies an attention-based memory component to generate specific relation vectors which can differentiate the influence from the aspect of interest, and applies a friend-level attention network to adaptively capture the preferences of users. For the POI contextual information, the POI neighbourhood module in MANC applies a feature-level attention network to capture the latent features of neighbourhood POIs, and applies a POI-level attention network to capture the geographical influence among POIs. Extensive experiments are carried out, and it is shown that the MANC model achieves better performance than other state-of-the-art methods.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Ahmadian S, Joorabloo N, Jalili M, Meghdadi M, Afsharchi M, Ren Y (2018) A temporal clustering approach for social recommender systems. In: Proceedings of the 10th international conference on advances in social networks analysis and mining, IEEE Computer Society, pp 1139–1144

  2. Ahmadian S, Afsharchi M, Meghdadi M (2019) An effective social recommendation method based on user reputation model and rating profile enhancement. J Inf Sci 45(5):607–642

    Article  Google Scholar 

  3. Ahmadian S, Joorabloo N, Jalili M, Ren Y, Meghdadi M, Afsharchi M (2020) A social recommender system based on reliable implicit relationships. Knowl Based Syst 192:105,371

    Article  Google Scholar 

  4. Bengio Y, Ducharme R, Vincent P, Jauvin C (2003) A neural probabilistic language model. J Mach Learn Res 3(2):1137–1155

    MATH  Google Scholar 

  5. Chang L, Shi Z, Gu T, Zhao L (2012) A family of dynamic description logics for representing and reasoning about actions. J Autom Reasoning 49(1):1–52

    Article  MathSciNet  Google Scholar 

  6. Chen C, Zhang M, Liu Y, Ma S (2018) Neural attentional rating regression with review-level explanations. In: Proceedings of the 2018 world wide Web conference international world wide Web conferences steering committee, pp 1583–1592

  7. Chen C, Zhang M, Liu Y, Ma S (2019) Social attentional memory network: Modeling aspect-and friend-level differences in recommendation. In: Proceedings of the 12th ACM international conference on Web search and data mining, ACM, pp 177–185

  8. Chen J, Zhang H, He X, Nie L, Liu W, Chua TS (2017) Attentive collaborative filtering: Multimedia recommendation with item-and component-level attention. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 335–344

  9. Chen Q, Xu J, Koltun V (2017) Fast image processing with fully-convolutional networks. In: Proceedings 2017 IEEE international conference on computer vision, IEEE Computer Society, pp 2516–2525

  10. Chen X, Xu H, Zhang Y, Tang J, Cao Y, Qin Z, Zha H (2018) Sequential recommendation with user memory networks. In: Proceedings of the 11th ACM international conference on Web search and data mining, ACM, pp 108–116

  11. Cheng C, Yang H, King I, Lyu MR (2012) Fused matrix factorization with geographical and social influence in location-based social networks. In: Proceedings of the 26th AAAI conference on artificial intelligence, pp 17–23

  12. Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12(7):2121–2159

    MathSciNet  MATH  Google Scholar 

  13. Ebesu T, Shen B, Fang Y (2018) Collaborative memory network for recommendation systems. In: Proceedings of the the 41st international ACM SIGIR conference on research and development in information retrieval, ACM, pp 515–524

  14. He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide Web, International World Wide Web Conferences Steering Committee, pp 173–182

  15. Hinton G, Deng L, Yu D, Dahl G, Ar Mohamed, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Kingsbury B et al (2012) Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine 29(6):82–97

    Article  Google Scholar 

  16. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. IEEE Computer 42(8):30–37

    Article  Google Scholar 

  17. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proceedings of the 26th annual conference on neural information processing systems, pp 1097–1105

  18. Li H, Ge Y, Hong R, Zhu H (2016) Point-of-interest recommendations: Learning potential check-ins from friends. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 975–984

  19. Li X, Cong G, Li XL, Pham TAN, Krishnaswamy S (2015) Rank-geofm: A ranking based geographical factorization method for point of interest recommendation. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 433–4421

  20. Lian D, Zhao C, Xie X, Sun G, Chen E, Rui Y (2014) Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 831–840

  21. Liu B, Fu Y, Yao Z, Xiong H (2013) Learning geographical preferences for point-of-interest recommendation. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 1043–1051

  22. Liu B, Xiong H, Papadimitriou S, Fu Y, Yao Z (2014) A general geographical probabilistic factor model for point of interest recommendation. IEEE Trans Knowl Data Eng 27 (5):1167– 1179

    Article  Google Scholar 

  23. Liu B, Su Y, Zha D, Gao N, Xiang J (2019) Carec: Content-aware point-of-interest recommendation via adaptive bayesian personalized ranking. Australian Journal of Intelligent Information Processing Systems 15(3):61–68

    Google Scholar 

  24. Liu Y, Wei W, Sun A, Miao C (2014) Exploiting geographical neighborhood characteristics for location recommendation. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management, ACM, pp 739–748

  25. Liu Y, Pham TAN, Cong G, Yuan Q (2017) An experimental evaluation of point-of-interest recommendation in location-based social networks. Proceedings of the VLDB Endowment 10(10):1010–1021

    Article  Google Scholar 

  26. Luong T, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 conference on empirical methods in natural language processing, the association for computational linguistics, pp 1412–1421

  27. Ma C, Zhang Y, Wang Q, Liu X (2018) Point-of-interest recommendation: Exploiting self-attentive autoencoders with neighbor-aware influence. In: Proceedings of the 27th ACM international conference on information and knowledge management, ACM, pp 697–706

  28. Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning, Omnipress, pp 807– 814

  29. Rahmani HA, Aliannejadi M, Ahmadian S, Baratchi M, Afsharchi M, Crestani F (2019) Lglmf: Local geographical based logistic matrix factorization model for POI recommendation. In: Proceedings of the 15th Asia information retrieval societies conference, Springer, pp 66–78

  30. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th conference on uncertainty in artificial intelligence, AUAI Press, pp 452–461

  31. Seo S, Huang J, Yang H, Liu Y (2017) Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In: Proceedings of the 11th ACM conference on recommender systems, ACM, pp 297–305

  32. Wang H, Shen H, Ouyang W, Cheng X (2018) Exploiting poi-specific geographical influence for point-of-interest recommendation. In: Proceedings of the 27th international joint conference on artificial intelligence, pp 3877–3883

  33. Weston J, Chopra S, Bordes A (2015) Memory networks. In: Proceedings of the 3rd international conference on learning representations

  34. Xie M, Yin H, Wang H, Xu F, Chen W, Wang S (2016) Learning graph-based poi embedding for location-based recommendation. In: Proceedings of the 25th ACM international on conference on information and knowledge management, ACM, pp 15–24

  35. Yang C, Bai L, Zhang C, Yuan Q, Han J (2017) Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 1245–1254

  36. Ye M, Yin P, Lee WC (2010) Location recommendation for location-based social networks. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, ACM, pp 458–461

  37. Ye M, Yin P, Lee WC, Lee DL (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 325–334

  38. Yin H, Zhou X, Shao Y, Wang H, Sadiq S (2015) Joint modeling of user check-in behaviors for point-of-interest recommendation. In: Proceedings of the 24th ACM international on conference on information and knowledge management, ACM, pp 1631– 1640

  39. Yochum P, Chang L, Gu T, Zhu M (2020) Linked open data in location-based recommendation system on tourism domain: a survey. IEEE Access 8:16,409–16,439

    Article  Google Scholar 

  40. Zhang JD, Chow CY (2015) Geosoca: Exploiting geographical, social and categorical correlations for point-of-interest recommendations. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 443–452

  41. Zhang JD, Chow CY, Li Y (2014) Lore: Exploiting sequential influence for location recommendations. In: Proceedings of the 22nd ACM SIGSPATIAL international conference on advances in geographic information Systems, ACM, pp 103–112

  42. Zhang S, Cheng H (2018) Exploiting context graph attention for poi recommendation in location-based social networks. In: Proceedings of the 23rd international conference on database systems for advanced applications, Springer, pp 83–99

  43. Zhang Z, Liu Y, Zhang Z, Shen B (2019) Fused matrix factorization with multi-tag, social and geographical influences for POI recommendation. World Wide Web 22(3):1135–1150

    Article  Google Scholar 

  44. Zhao S, Zhao T, Yang H, Lyu MR, King I (2016) Stellar: spatial-temporal latent ranking for successive point-of-interest recommendation. In: Proceedings of the 30th AAAI conference on artificial intelligence, AAAI Press, pp 315–322

  45. Zhao S, Zhao T, King I, Lyu MR (2017) Geo-teaser: Geo-temporal sequential embedding rank for point-of-interest recommendation. In: Proceedings of the 26th international conference on world wide Web companion, International World Wide Web Conferences Steering Committee, pp 153–162

  46. Zheng L, Noroozi V, Yu PS (2017) Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the 10th ACM international conference on Web search and data mining, ACM, pp 425–434

Download references

Acknowledgements

This work is supported by the Natural Science Foundation of China (Nos. U1811264,U1711263,61966009), the Natural Science Foundation of Guangxi Province (No. 2018GXNSFDA281045, No. 2020GXNSFAA159055), and the Guangxi Innovation-Driven Development Project (No. AA17202024).

Author information

Authors and Affiliations

  1. Guangxi Key lab of Trusted Software, Guilin University of Electronic Technology, Guilin, China

    Liang Chang, Jianbo Huang, Chenzhong Bin & Wenkai Wang

  2. Center of Guangxi Province for Collaborative Innovation in the Technology of IOT and the Industrialization, Guilin University of Electronic Technology, Guilin, China

    Liang Chang

  3. Beijing Jiaotong University, Beijing, China

    Wei Chen

Authors
  1. Liang Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianbo Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chenzhong Bin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenkai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Chen.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, L., Chen, W., Huang, J. et al. Exploiting multi-attention network with contextual influence for point-of-interest recommendation. Appl Intell 51, 1904–1917 (2021). https://doi.org/10.1007/s10489-020-01868-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-020-01868-0

Keywords

Search

Navigation