A New Similarity Method for Tourism Recommendation Systems

Abstract

In this paper, we proposed a new similarity method to use in tourism recommendation systems. Recommendation systems highly depend on the existence of a similarity measure used to identify similar items. In tourism products such as hotels, trips, packages are all hard to judge for their similarity. The proposed method is simply based on user defined weights to calculate similarity. First, we represented each product as a vector and then weighted by user defined scores. Then it uses cosine similarity to measure similarity between items. We evaluated our method using a dataset created by the travel expert. Our experimental results indicate that the proposed method achieves a significant improvement in terms of mean average precision (MAP). We conclude that the proposed method is a promising approach for improving the performance of tourism recommendation systems.

Keywords

• similarity method
• tourism recommendation system
• cosine similarity

References

1. [1] Kesici, E., & Öztürk, O. (2020). A hybrid movie recommendation system based on item similarity and user preferences. Journal of Intelligent & Fuzzy Systems, 39(2), 1603-1612. https://doi.org/10.3233/JIFS-179469
2. [2] Burke, R. (2018). Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, 28(4-5), 331-388. https://doi.org/10.1007/s11257-018-9195-x
3. [3] Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51-65. doi: 10.1016/j.tourman.2016.10.015.
4. [4] Chen, Y., Wu, B., Zhang, X., & Zhang, Z. (2018). A personalized travel recommendation system using long-tail correlations. Journal of Travel Research, 57(1), 31-44. doi: 10.1177/0047287517696563.
5. [5] Kim, J. H., & Han, J. Y. (2019). Personalization in travel recommendation systems: An exploratory study on the role of contextual factors. Journal of Travel Research, 58(1), 85-99. doi: 10.1177/0047287517733559.
6. [6] Li, X., Liang, Y., Huang, Z., & Huang, Y. (2019). Deep learning for travel recommendation: An end-to-end neural recommendation approach on multi-source data. IEEE Transactions on Knowledge and Data Engineering, 31(12), 2392-2405. doi: 10.1109/TKDE.2019.2929371.
7. [7] Wang, J., Zhang, J., Zhang, X., Liu, Q., & Zhang, X. (2020). A novel travel recommendation algorithm based on deep learning with context awareness. IEEE Access, 8, 1-14. doi: 10.1109/ACCESS.2020.2981155.
8. [8] Chen, C., Wu, Y., & Buhalis, D. (2021). A critical review of smart tourism destination literature and future research directions. Journal of Hospitality and Tourism Technology, 12(2), 202-221. doi: 10.1108/JHTT-08-2019-0131.

9. [9] Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37. doi: 10.1109/MC.2009.263.
10. [10] Zhang, L., Wang, D., Chen, X., & Huang, J. (2019). Travel recommendation: A survey of recent advances and future directions. Journal of Hospitality and Tourism Research, 43(3), 421-463. doi: 10.1177/1096348019857687.
11. [11] Li, X., Wang, J., Zhang, Y., & Liu, Y. (2019). An intelligent recommendation algorithm based on improved collaborative filtering. IEEE Access, 7, 160293-160304. doi: 10.1109/ACCESS.2019.2951277.
12. [12] Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51-65. doi: 10.1016/j.tourman.2016.10.015.
13. [13] Ma, S., Liu, X., Li, Y., Huang, X., & Li, G. (2018). Social influence-based collaborative filtering recommendation algorithm. IEEE Access, 6, 38885-38893. doi: 10.1109/ACCESS.2018.2859886.
14. [14] Liu, X., Li, Y., Zhou, T., & Li, J. (2016). A hybrid similarity measure for collaborative filtering recommender systems in the tourism domain. Tourism Management, 54, 87-101. doi: 10.1016/j.tourman.2015.11.012.
15. [15] Ye, H., Yang, L., Wang, X., & Law, R. (2011). Contextual collaborative filtering based on Bayes probability model for personalized service recommendation in ubiquitous commerce. Expert Systems with Applications, 38(5), 5850-5859. doi: 10.1016/j.eswa.2010.11.129
16. [16] Zhang, X., Zheng, Y., & Lyu, M. R. (2018). Deep learning-based recommender system: A survey and new perspectives. ACM Computing Surveys, 52(1), 1-38. doi: 10.1145/3137597
17. [17] Feng, S., Huang, Y., & Zhang, Y. (2019). A tourism recommendation algorithm based on Dirichlet distribution and item attributes. IEEE Access, 7, 41434-41444. doi: 10.1109/ACCESS.2019.2908521
18. [18] Ma, X., Lv, P., Wang, P., & Wang, Y. (2021). An improved Dirichlet-based similarity measure for tourism recommendation. Journal of Ambient Intelligence and Humanized Computing, 12(9), 9275-9287. doi: 10.1007/s12652-021-03398-1.
19. [19] Liu, X., Liu, J., & Lu, X. (2010). A trust-based recommendation algorithm for tourism. Expert Systems with Applications, 37(12), 8460-8468.
20. [20] Li, X., Wang, D., Zhang, J., & Chen, H. (2014). An intelligent recommendation system for tourism planning based on social network analysis and ontology. Journal of Computer and System Sciences, 80(7), 1364-1376.
21. [21] Wang, L., Zhang, Z., & Liu, Y. (2012). Active learning for content-based recommendation: An exploratory study. Journal of Computer Information Systems, 53(1), 14-23.
22. [22] Wang, L., Zhang, Z., & Liu, Y. (2014). Active learning framework for content-based recommendation. In Proceedings of the 2014 IEEE International Conference on Systems, Man, and Cybernetics (pp. 151-156). IEEE.
23. [23] Jin, X., Xiang, Z., Du, Q., & Ma, Y. (2019). Privacy protection in tourism big data: a research agenda. Journal of Travel Research, 58(6), 963-977. https://doi.org/10.1177/0047287518809143
24. [24] Zhou, Y., Zhang, Y., Liu, H., & Hu, J. (2019). Privacy preserving recommendation systems: a survey. Journal of Big Data, 6(1), 1-29. https://doi.org/10.1186/s40537-019-0194-2