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

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Abstract

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 TripAdvisor.com demonstrate that the proposed method captures an accuracy of 89.5% and outperforms the other recent related algorithms.

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References

  1. Chang Z, Arefin MS, Morimoto Y (2013) Hotel recommendation based on surrounding environments. Second IIAI International Conference on Advanced Applied Informatics, 330-336

  2. Chang JH, Tsai CE, Chiang JH (2018) Using heterogeneous social media as auxiliary information to improve hotel recommendation performance. IEEE Access 6:42647–42660

    Article  Google Scholar 

  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), 125-128

  4. Hasan MA, Zaki MJ (2011) A survey of link prediction in social networks. In Social network data analytics, pp 243-275. Springer, Boston

    Chapter  Google Scholar 

  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. IEEE

  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), 317-320

  7. Jinzhu Z (2017) Uncovering mechanisms of co-authorship evolution by multirelations-based link prediction. Inf Process Manag 53(1):42–51

    Article  Google Scholar 

  8. Kaya B, Poyraz M (2014) Supervised link prediction in symptom networks with evolving case. Measurement 56:231–238

    Article  Google Scholar 

  9. Kaya B, Poyraz M (2015) Age-series based link prediction in evolving disease networks. Comput Biol Med 63:1–10

    Article  Google Scholar 

  10. Kim J, Hastak M (2018) Social network analysis. Int J Inf Manag 38(1):86–96

    Article  Google Scholar 

  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):5826–5841

    Article  MathSciNet  Google Scholar 

  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):2022–2037

    Article  MathSciNet  Google Scholar 

  13. Lan X, Ye M, Zhang S, Zhou H, Yuen PC (2018b) Modality-correlation-aware sparse representation for RGB-infrared object tracking. Pattern Recogn Lett

  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 Electron

  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 7:67761–67771

    Article  Google Scholar 

  16. Lee PJ, Hu YH, Lu KT (2018) Assessing the helpfulness of online hotel reviews: a classification-based approach. Telematics Inform 35(2):436–445

    Article  Google Scholar 

  17. Newman MEJ (2001) Clustering and preferential attachment in growing networks. Phys Rev E 64:025102

    Article  Google Scholar 

  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 28:168–179

    Article  Google Scholar 

  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):021102

    Article  Google Scholar 

  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. ACM

  21. Scott J (2017) Social network analysis. SAGE Publications Ltd., Thousand Oaks

    Book  Google Scholar 

  22. Shi C, Li Y, Zhang J, Yu PS (2017) A survey of heterogeneous information network analysis. IEEE Trans Knowl Data Eng 29(1):17–37

    Article  Google Scholar 

  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.

  24. Tan P-N, Steinbach M, Kumar V (2005) Introduction to Data Mining. Addison Wesley, Boston

    Google Scholar 

  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):475–489

    Article  Google Scholar 

  26. Veloso BM, Leal F, Malheiro B, Burguillo JC (2019) On-line guest profiling and hotel recommendation. Electron Commer Res Appl 34:100832

    Article  Google Scholar 

  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):381–394

    Article  Google Scholar 

  28. Wu J, Zhang G, Ren Y (2017) A balanced modularity maximization link prediction model in social networks. Inf Process Manag 53(1):295–307

    Article  Google Scholar 

  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)

  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.

  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 2015:9

  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. IEEE

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  1. Department of Electronics and Automation, Fırat University, Elazığ, Turkey

    Buket Kaya

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Correspondence to Buket Kaya.

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Kaya, B. A hotel recommendation system based on customer location: a link prediction approach. Multimed Tools Appl 79, 1745–1758 (2020). https://doi.org/10.1007/s11042-019-08270-0

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