Skip to main content

A multi-criteria point of interest recommendation using the dominance concept

Abstract

The learning similarity between users and points of interests (POIs) is an important function in location-based social networks (LBSN), which could primarily benefit multiple location-based services, especially in terms of POI recommendation. As one of the well-known recommender technologies, Collaborative Filtering (CF) has been employed to a great extent in literature, due to its simplicity and interpretability. However, it is facing a great challenge in generating accurate similarities between users or items, because of data sparsity. Traditional similarity measures which rely on explicit user feedback (e.g., rating) are not applicable for implicit feedback (e.g., check-ins). In this study, we propose multi-criteria user–user and POI–POI similarity measures, based on the dominance concept. In this regard, we incorporate geographical, temporal, social, preferential and textual criteria into the similarity measures of CF. We are interested in measuring POI similarity from a location perspective, by taking into account the influence of the dominance concept on multiple dimensions of POIs. To evaluate the effectiveness of our method, a series of experiments are conducted with a large-scale real dataset, collected from the Foursquare of two cities in terms of POI recommendation. Experimental results revealed that the proposed method significantly outperforms the existing state-of-the-art alternatives. A further experiment demonstrates the superiority of the proposed method in alleviating sparsity and handling the cold-start problem affecting POI recommendation.

This is a preview of subscription content, access via your institution.

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

References

  1. Abbass HA, Sarker R, Newton C (2001) PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the 2001 Congress on evolutionary computation (IEEE Cat. No. 01TH8546), pp 971–978

  2. Adomavicius G, Kwon Y (2007) New recommendation techniques for multicriteria rating systems. IEEE Intell Syst 22(3):48–55

    Article  Google Scholar 

  3. Ahn HJ (2008) A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf Sci 178(1):37–51

    Article  Google Scholar 

  4. Bao J, Zheng Y, Mokbel MF (2012) Location-based and preference-aware recommendation using sparse geo-social networking data. In: Proceedings of the 20th international conference on advances in geographic information systems, pp 199–208

  5. Cai L, Xu J, Liu J, Pei T (2018) Integrating spatial and temporal contexts into a factorization model for POI recommendation. Int J Geogr Inf Sci 32(3):524–546

    Article  Google Scholar 

  6. Cheng C, Yang H, King I, Lyu MR (2012) Fused matrix factorization with geographical and social influence in location-based social networks. In: Twenty-Sixth AAAI conference on artificial intelligence

  7. Clements M, Serdyukov P, De Vries AP, Reinders MJ (2010) Finding wormholes with flickr geotags. In: European conference on information retrieval, pp 658–661

  8. Dao TH, Jeong SR, Ahn H (2012) A novel recommendation model of location-based advertising: Context-Aware Collaborative Filtering using GA approach. Expert Syst Appl 39(3):3731–3739

    Article  Google Scholar 

  9. Davtalab M, Alesheikh AA (2021) A POI recommendation approach integrating social spatio-temporal information into probabilistic matrix factorization. Knowl Inf Syst 63(1):65–85

    Article  Google Scholar 

  10. Descioli P, Kurzban R, Koch EN, Liben-Nowell D (2011) Best friends: alliances, friend ranking, and the MySpace social network. Perspect Psychol Sci 6(1):6–8

    Article  Google Scholar 

  11. Emrich A, Chapko A, Werth D, Loos P (2013) Adaptive, multi-criteria recommendations for location-based services. In: 2013 46th Hawaii international conference on system sciences, pp 1165–1173

  12. Feng C, Liang J, Song P, Wang Z (2020) A fusion collaborative filtering method for sparse data in recommender systems. Inf Sci 521:365–379

    MathSciNet  Article  Google Scholar 

  13. Gan M, Gao L (2019) Discovering memory-based preferences for poi recommendation in location-based social networks. ISPRS Int J Geo Inf 8(6):279

    Article  Google Scholar 

  14. Gao H, Xu Y, Yin Y, Zhang W, Li R, Wang X (2019) Context-aware QoS prediction with neural collaborative filtering for Internet-of-Things services. IEEE Internet of Things J 7:4532–4542

    Article  Google Scholar 

  15. Gao K, Yang X, Wu C, Qiao T, Chen X, Yang M, Chen L (2020) Exploiting location-based context for POI recommendation when traveling to a new region. IEEE Access 8:52404–52412

    Article  Google Scholar 

  16. Geng B, Jiao L, Gong M, Li L, Wu Y (2019) A two-step personalized location recommendation based on multi-objective immune algorithm. Inf Sci 475:161–181

    Article  Google Scholar 

  17. Guo L, Jiang H, Wang X, Liu F (2017) Learning to recommend point-of-interest with the weighted Bayesian personalized ranking method in LBSNs. Information 8(1):20

    Article  Google Scholar 

  18. Hasan M, Roy F (2019) An item-item collaborative filtering recommender system using trust and genre to address the cold-start problem. Big Data Cognit Comput 3(3):39

    Article  Google Scholar 

  19. He J, Qi J, Ramamohanarao K (2019) A joint context-aware embedding for trip recommendations. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE), pp 292–303

  20. Huang H, Gartner G (2014) Using trajectories for collaborative filtering-based POI recommendation. IJDMMM 6(4):333–346

    Article  Google Scholar 

  21. Keeney RL (1996) Value-focused thinking. Harvard University Press

    MATH  Google Scholar 

  22. Levandoski JJ, Sarwat M, Eldawy A, Mokbel MF (2012) Lars: A location-aware recommender system. In: 2012 IEEE 28th International conference on data engineering, pp 450–461

  23. Lewis K, Gonzalez M, Kaufman J (2012) Social selection and peer influence in an online social network. Proc Natl Acad Sci 109(1):68–72

    Article  Google Scholar 

  24. Liu J, Wu W (2011) Coevolutionary optimization algorithm: with ecological competition model. In: International Conference on artificial intelligence and computational intelligence, pp 68–75

  25. Liu L, Mehandjiev N, Xu D-L (2011) Multi-criteria service recommendation based on user criteria preferences. In: Proceedings of the fifth ACM conference on recommender systems, pp 77–84

  26. Liu J, Wang W, Chen Z, Du X, Qi Q (2012) A novel user-based collaborative filtering method by inferring tag ratings. ACM SIGAPP Appl Comput Rev 12(4):48–57

    Article  Google Scholar 

  27. 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, pp 1043–1051

  28. Liu H, Hu Z, Mian A, Tian H, Zhu X (2014a) A new user similarity model to improve the accuracy of collaborative filtering. Knowl-Based Syst 56:156–166

    Article  Google Scholar 

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

  30. Liu B, Meng Q, Zhang H, Xu K, Cao J (2020) VGMF: visual contents and geographical influence enhanced point‐of‐interest recommendation in location‐based social network. Trans Emerg Telecommun Technol, p e3889. https://doi.org/10.1002/ett.3889

  31. Lu Y-S, Huang J-L (2020) GLR: A graph-based latent representation model for successive POI recommendation. Future Gener Comput Syst 102:230–244

    Article  Google Scholar 

  32. Luan W, Liu G, Jiang C, Qi L (2017) Partition-based collaborative tensor factorization for POI recommendation. IEEE/CAA J Autom Sin 4(3):437–446

    MathSciNet  Article  Google Scholar 

  33. Lyu Y, Chow C-Y, Wang R, Lee VC (2014) Using multi-criteria decision making for personalized point-of-interest recommendations. In: Proceedings of the 22nd ACM SIGSPATIAL international conference on advances in geographic information systems, pp 461–464

  34. Lyu Y, Chow C-Y, Wang R, Lee VC (2019) iMCRec: a multi-criteria framework for personalized point-of-interest recommendations. Inf Sci 483:294–312

    Article  Google Scholar 

  35. Manouselis N, Costopoulou C (2007) Experimental analysis of design choices in multiattribute utility collaborative filtering. Int J Pattern Recognit Artif Intell 21(02):311–331

    Article  Google Scholar 

  36. Murata T, Ishibuchi H (1995) MOGA: multi-objective genetic algorithms. IEEE international conference on evolutionary computation, pp 289–294

  37. Naak A, Hage H, Aimeur E (2009) A multi-criteria collaborative filtering approach for research paper recommendation in papyres. In: International conference on e-technologies, pp 25–39

  38. Nilashi M, Bin Ibrahim O, Ithnin N (2014) Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Syst Appl 41(8):3879–3900

    Article  Google Scholar 

  39. Nilashi M, Ahani A, Esfahani MD, Yadegaridehkordi E, Samad S, Ibrahim O, Sharef NM, Akbari E (2019) Preference learning for eco-friendly hotels recommendation: a multi-criteria collaborative filtering approach. J Clean Prod 215:767–783

    Article  Google Scholar 

  40. Ortega F, Sánchez J-L, Bobadilla J, Gutiérrez A (2013) Improving collaborative filtering-based recommender systems results using Pareto dominance. Inf Sci 239:50–61

    Article  Google Scholar 

  41. Qiao Y, Luo X, Li C, Tian H, Ma J (2020) Heterogeneous graph-based joint representation learning for users and POIs in location-based social network. Inf Process Manag 57(2):102151

    Article  Google Scholar 

  42. Rahmani HA, Aliannejadi M, Ahmadian S, Baratchi M, Afsharchi M, Crestani F (2019a) LGLMF: local geographical based logistic matrix factorization model for POI recommendation. arXiv preprint arXiv:1909.06667

  43. Rahmani HA, Aliannejadi M, Mirzaei Zadeh R, Baratchi M, Afsharchi M, Crestani F (2019b) Category-aware location embedding for point-of-interest recommendation. In: Proceedings of the 2019 ACM SIGIR international conference on theory of information retrieval, pp 173–176

  44. Rahmani HA, Aliannejadi M, Baratchi M, Crestani F (2020) Joint geographical and temporal modeling based on matrix factorization for point-of-interest recommendation. arXiv preprint arXiv:2001.08961

  45. Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on computer supported cooperative work, pp 175–186

  46. Sarwar BM, Karypis G, Konstan JA, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. Www 1:285–295

    Google Scholar 

  47. Sarwat M, Levandoski JJ, Eldawy A, Mokbel MF (2014) Lars*: an efficient and scalable location-aware recommender system. IEEE Trans Know Data Eng 26(6):1384–1399

    Article  Google Scholar 

  48. Song C, Qu Z, Blumm N, Barabási A-L (2010) Limits of predictability in human mobility. Science 327(5968):1018–1021

    MathSciNet  Article  Google Scholar 

  49. Su Y, Li X, Liu B, Zha D, Xiang J, Tang W, Gao N (2020) FGCRec: Fine-grained geographical characteristics modeling for point-of-interest recommendation. In: ICC 2020–2020 IEEE international conference on communications (ICC), pp 1–6

  50. Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46(sup1):234–240

    Article  Google Scholar 

  51. Wang Y, Yuan NJ, Lian D, Xu L, Xie X, Chen E, Rui Y (2015) Regularity and conformity: location prediction using heterogeneous mobility data. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1275–1284

  52. Wang X, Salim FD, Ren Y, Koniusz P (2020) Relation embedding for personalised POI recommendation. arXiv preprint arXiv:2002.03461

  53. Xiong X, Qiao S, Han N, Xiong F, Bu Z, Li R-H, Yue K, Yuan G (2020) Where to go: An effective point-of-interest recommendation framework for heterogeneous social networks. Neurocomputing 373:56–69

    Article  Google Scholar 

  54. 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, pp 1245–1254

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

  56. Ye M, Yin P, Lee W-C, Lee D-L (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, pp 325–334

  57. Yin H, Sun Y, Cui B, Hu Z, Chen L (2013) LCARS: a location-content-aware recommender system. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 221–229

  58. Yin H, Cui B, Chen L, Hu Z, Zhou X (2015) Dynamic user modeling in social media systems. ACM Trans Inf Syst (TOIS) 33(3):1–44

    Article  Google Scholar 

  59. Yin H, Cui B, Zhou X, Wang W, Huang Z, Sadiq S (2016) Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. ACM Trans Inf Syst (TOIS) 35(2):1–44

    Article  Google Scholar 

  60. Ying J-C, Chen H-S, Lin KW, Lu EH-C, Tseng VS, Tsai H-W, Cheng KH, Lin S-C (2014) Semantic trajectory-based high utility item recommendation system. Expert Syst Appl 41(10):4762–4776

    Article  Google Scholar 

  61. Yu D, Wanyan W, Wang D (2021) Leveraging contextual influence and user preferences for point-of-interest recommendation. Multimed Tools Appl 80(1):1487–1501

    Article  Google Scholar 

  62. Zeng J, Li Y, Li F, Wen J, Hirokawa S (2017) A point-of-interest recommendation method using location similarity. In: 2017 6th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), pp 436–440

  63. Zhang S, Cheng H (2018) Exploiting context graph attention for POI recommendation in location-based social networks. International conference on database systems for advanced applications, pp 83–99

  64. Zhang J-D, Chow C-Y (2013) iGSLR: personalized geo-social location recommendation: a kernel density estimation approach. In: Proceedings of the 21st ACM SIGSPATIAL international conference on advances in geographic information systems, pp 334–343

  65. Zhang J-D, Chow C-Y (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, pp 443–452

  66. Zhang J-D, Chow C-Y, Zheng Y (2015) ORec: an opinion-based point-of-interest recommendation framework. In: Proceedings of the 24th ACM international on conference on information and knowledge management, pp 1641–1650

  67. Zhao Y-L, Nie L, Wang X, Chua T-S (2014) Personalized recommendations of locally interesting venues to tourists via cross-region community matching. ACM Trans Intell Syst Technol (TIST) 5(3):1–26

    Article  Google Scholar 

  68. Zhao S, King I, Lyu MR (2016) A survey of point-of-interest recommendation in location-based social networks. arXiv preprint arXiv:1607.00647

  69. Zheng Y (2012) Tutorial on location-based social networks. In: Proceedings of the 21st International Conference on World wide web, WWW, Citeseer

  70. Zheng Y, Chen Y, Xie X, Ma W-Y (2009) GeoLife2.0: a location-based social networking service. In: 2009 Tenth international conference on mobile data management: systems, services and middleware pp 357–358

  71. Zheng VW, Cao B, Zheng Y, Xie X, Yang Q (2010) Collaborative filtering meets mobile recommendation: a user-centered approach. In: Twenty-Fourth AAAI Conference on Artificial Intelligence

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ali Asghar Alesheikh.

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

Verify currency and authenticity via CrossMark

Cite this article

Davtalab, M., Alesheikh, A.A. A multi-criteria point of interest recommendation using the dominance concept. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-03533-x

Download citation

Keywords

  • Collaborative filtering
  • POI recommendation
  • Similarity learning
  • Dominance concept
  • LBSN