A hotel recommendation system based on customer location: a link prediction approach

  • Buket KayaEmail author


Hotel recommendation is one of the most used application areas in recommendation systems. So far, many hotel recommendation systems have been proposed. Most of these systems are based collaborative filtering, content-based filtering, and association rule methods and employ the features of hotel, the ratings given by user, online reviews and comments in social network about the related hotel as data. However, due to the difficulty of processing the data used, the performance rates and speeds of these methods are relatively slow. As a solution to these problems in this paper, we propose a novel hotel recommendation system based on link prediction method. For this purpose, a customer-hotel bipartite network was first constructed and the relationship information in this network was used as data. Then, a supervised link prediction method that consider customers’ location was presented. To the best of our knowledge, this is the first study that recommends hotel by using link prediction method. The experimental results conducted on data crawled from demonstrate that the proposed method captures an accuracy of 89.5% and outperforms the other recent related algorithms.


Hotel recommendation Customer location Customer-hotel bipartite network Link prediction 



  1. 1.
    Chang Z, Arefin MS, Morimoto Y (2013) Hotel recommendation based on surrounding environments. Second IIAI International Conference on Advanced Applied Informatics, 330-336Google Scholar
  2. 2.
    Chang JH, Tsai CE, Chiang JH (2018) Using heterogeneous social media as auxiliary information to improve hotel recommendation performance. IEEE Access 6:42647–42660CrossRefGoogle Scholar
  3. 3.
    Gundogan E, Kaya B (2017) A recommendation method based on link prediction in drug-disease bipartite network. 2nd International Conference on Advanced Information and Communication Technologies (AICT), 125-128Google Scholar
  4. 4.
    Hasan MA, Zaki MJ (2011) A survey of link prediction in social networks. In Social network data analytics, pp 243-275. Springer, BostonCrossRefGoogle Scholar
  5. 5.
    Hecking T, Steinert L, Gohnert T, Hoppe HU (2014) Incremental clustering of dynamic bipartite network. In 2014 European Network Intelligence Conference, pp 9-16. IEEEGoogle Scholar
  6. 6.
    Huming G, Weili L (2010) A hotel recommendation system based on collaborative filtering and Rankboost algorithm. Second International Conference on Multimedia and Information Technology (MMIT), 317-320Google Scholar
  7. 7.
    Jinzhu Z (2017) Uncovering mechanisms of co-authorship evolution by multirelations-based link prediction. Inf Process Manag 53(1):42–51CrossRefGoogle Scholar
  8. 8.
    Kaya B, Poyraz M (2014) Supervised link prediction in symptom networks with evolving case. Measurement 56:231–238CrossRefGoogle Scholar
  9. 9.
    Kaya B, Poyraz M (2015) Age-series based link prediction in evolving disease networks. Comput Biol Med 63:1–10CrossRefGoogle Scholar
  10. 10.
    Kim J, Hastak M (2018) Social network analysis. Int J Inf Manag 38(1):86–96CrossRefGoogle Scholar
  11. 11.
    Lan X, Ma AJ, Yuen PC, Chellappa R (2015) Joint sparse representation and robust feature-level fusion for multi-cue visual tracking. IEEE Trans Image Process 24(12):5826–5841MathSciNetCrossRefGoogle Scholar
  12. 12.
    Lan X, Zhang S, Yuen PC, Chellappa R (2018a) Learning common and feature-specific patterns: a novel multiple-sparse-representation-based tracker. IEEE Trans Image Process 27(4):2022–2037MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lan X, Ye M, Zhang S, Zhou H, Yuen PC (2018b) Modality-correlation-aware sparse representation for RGB-infrared object tracking. Pattern Recogn LettGoogle Scholar
  14. 14.
    Lan X, Ye M, Shao R, Zhong B, Yuen PC, Zhou H (2019a) Learning modality-consistency feature templates: a robust RGB-infrared tracking system. IEEE Trans Ind ElectronGoogle Scholar
  15. 15.
    Lan X, Ye M, Shao R, Zhong B, Jain DK, Zhou H (2019b) Online non-negative multi-modality feature template learning for RGB-assisted infrared tracking. IEEE Access 7:67761–67771CrossRefGoogle Scholar
  16. 16.
    Lee PJ, Hu YH, Lu KT (2018) Assessing the helpfulness of online hotel reviews: a classification-based approach. Telematics Inform 35(2):436–445CrossRefGoogle Scholar
  17. 17.
    Newman MEJ (2001) Clustering and preferential attachment in growing networks. Phys Rev E 64:025102CrossRefGoogle Scholar
  18. 18.
    Nilashi M, Ibrahim O, Yadegaridehkordi E, Samad S, Akbari E, Alizadeh A (2018) Travelers decision making using online review in social network sites: a case on TripAdvisor. J Comput Sci 28:168–179CrossRefGoogle Scholar
  19. 19.
    Ou Q, Jin Y-D, Zhou T, Wang B-H, Yin B-Q (2007) Power-law strength-degree correlation from resource-allocation dynamics on weighted network. Phys Rev E 75(2 pt 1):021102CrossRefGoogle Scholar
  20. 20.
    Saleem MA, Kumar R, Calders T, Xie X, Pederson TB (2017) Location influence in location-based social networks. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp 621-630. ACMGoogle Scholar
  21. 21.
    Scott J (2017) Social network analysis. SAGE Publications Ltd., Thousand OaksGoogle Scholar
  22. 22.
    Shi C, Li Y, Zhang J, Yu PS (2017) A survey of heterogeneous information network analysis. IEEE Trans Knowl Data Eng 29(1):17–37CrossRefGoogle Scholar
  23. 23.
    Takuma K, Yamamoto J, Kamei S, Fujita S (2016) A hotel recommendation system based on reviews: what do you attach importance to? Fourth International Symposium on Computing and Networking (CANDAR), 710-712.Google Scholar
  24. 24.
    Tan P-N, Steinbach M, Kumar V (2005) Introduction to Data Mining. Addison Wesley, BostonGoogle Scholar
  25. 25.
    Valderde-Rebeza JC, Roche M, Poncelet P, Lopes AA (2018) The role of location and social strength for friendship prediction in location-based social network. Inf Process Manag 54(4):475–489CrossRefGoogle Scholar
  26. 26.
    Veloso BM, Leal F, Malheiro B, Burguillo JC (2019) On-line guest profiling and hotel recommendation. Electron Commer Res Appl 34:100832CrossRefGoogle Scholar
  27. 27.
    Wang JQ, Zhang X, Zhang HY (2018) Hotel recommendation approach based on the online consumer reviews using interval neutrosophic linguistic numbers. J Intell Fuzzy Syst 34(1):381–394CrossRefGoogle Scholar
  28. 28.
    Wu J, Zhang G, Ren Y (2017) A balanced modularity maximization link prediction model in social networks. Inf Process Manag 53(1):295–307CrossRefGoogle Scholar
  29. 29.
    Xiong YN, Geng LX (2010) Personalized intelligent hotel recommendation system for online reservation--A perspective of product and user characteristics. International Conference on Management and Service Science (MASS)Google Scholar
  30. 30.
    Zhang K, Wang K, Wang X, Jin C, Zhou A (2015) Hotel recommendation based on user preference analysis. 31st IEEE International Conference on Data Engineering Workshops, 134-138.Google Scholar
  31. 31.
    Zou Q, Li J, Hong Q, Lin Z, Wu Y, Shi H, Ju Y (2015) Prediction of microRNA-disease associations based on social network analysis methods. BioMed Res Int 2015:9Google Scholar
  32. 32.
    Zulkefli NABM, Baharudin BB (2015) Hotel travel recommendation based on blog information. In 2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC), pp 243-248. IEEEGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and AutomationFırat UniversityElazığTurkey

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