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Exploring Hybrid Recommender Systems for Personalized Travel Applications

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Cognitive Informatics and Soft Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 768))

Abstract

The recent research in the recommender systems domain has attracted many researchers due to its increasing demands in the real world. To bridge the real-world issues of the users with the problems of the researchers in the digital world, we present hybrid recommendation techniques in e-Tourism domain. In this paper, we have explained the research problems in the e-Tourism applications and presented the possible solution to achieve better personalized recommendations. We have developed a Personalized Context-Aware Hybrid Travel Recommender System (PCAHTRS) by incorporating user’s contextual information. The proposed PCAHTRS is evaluated on the real-time large-scale datasets of Yelp and TripAdvisor. The experimental results depict the improved performance of the proposed approach over traditional approaches. We have concluded the paper with future work guidelines to help researchers to achieve fruitful solutions for recommendation problems.

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References

  1. Batet, M., Moreno, A., Sánchez, D., Isern, D., Valls, A.: Turist@: Agent-based personalised recommendation of touristic activities. Expert Syst. Appl. 39, 7319–7329 (2012)

    Article  Google Scholar 

  2. Huang, Y., Bian, L.: A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet. Expert Syst. Appl. 36, 933–943 (2009)

    Article  Google Scholar 

  3. Niaraki, A.S., Kim, K.: Ontology based personalized route planning system using a multi-criteria decision making approach. Expert Syst. Appl. 36, 2250–2259 (2009)

    Article  Google Scholar 

  4. Noguera, J.M., Barranco, M.J., Segura, R.J., Martínez, L.: A mobile 3D-GIS hybrid recommender system for tourism. Inf. Sci. 215, 37–52 (2012)

    Article  Google Scholar 

  5. Agalya, D., Subramaniyaswamy, V.: Group-Aware recommendation using random forest classification for sparsity problem. Indian J. Sci Technol. 9(48), art. no. 107960 (2016)

    Google Scholar 

  6. Saipraba, N., Subramaniyaswamy, V.: Enhancing stability of recommender system: an ensemble based information retrieval approach. Indian J. Sci. Technol. 9(48), art. no. 107979 (2016)

    Google Scholar 

  7. Logesh, R., Subramaniyaswamy, V.: A reliable point of interest recommendation based on trust relevancy between users. Wireless Pers. Commun. 97(2), 2751–2780 (2017)

    Article  Google Scholar 

  8. Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Gao, X.Z., Indragandhi, V.: A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city. Future Gener. Comput. Syst. 1–38 (2017)

    Google Scholar 

  9. Logesh, R., Subramaniyaswamy, V.: Learning recency and inferring associations in location based social network for emotion induced Point-Of-Interest recommendation. J. Inf. Sci. Eng. 33(6), 1629–1647 (2017)

    Google Scholar 

  10. Logesh, R., Subramaniyaswamy, V.: A collaborative location based travel recommendation system through enhanced rating prediction for the group of users. Comput. Intell. Neurosci. 1–28, art. no. 1291358 (2016)

    Google Scholar 

  11. Logesh, R., Subramaniyaswamy, V., Malathi, D., Senthilselvan, N., Sasikumar, A., Saravanan, P., Manikandan, G.: Dynamic particle swarm optimization for personalized recommender system based on electroencephalography feedback. Biomed. Res. 28(13), 5646–5650 (2017)

    Google Scholar 

  12. Logesh, R., Subramaniyaswamy, V., Vijayakumar, V.: A personalized travel recommender system through utilizing social network profile and accurate GPS data. Electron. Gov. Int. J. (2017)

    Google Scholar 

  13. Subramaniyaswamy, V., Logesh, R.: Adaptive KNN based recommender system through mining of user preferences. Wireless Pers. Commun. 97(2), 2229–2247 (2017)

    Article  Google Scholar 

  14. Subramaniyaswamy, V., Logesh, R., Abejith, M., Sunil, U., Umamakeswari, A.: Sentiment analysis of tweets for estimating criticality and security of events. J. Organ. End User Comput. 29(4), 1–20 (2017)

    Article  Google Scholar 

  15. Subramaniyaswamy, V., Vijayakumar, V., Indragandhi, V., Logesh, R.: Data mining-based tag recommendation system: an overview. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 5(3), 87–112 (2015)

    Article  Google Scholar 

  16. Subramaniyaswamy, V., Logesh, R., Chandrashekhar, M., Challa, A., Vijayakumar, V.: A personalised movie recommendation system based on collaborative filtering. Int. J. High Perform. Comput. Networking 10(1–2), 54–63 (2017)

    Article  Google Scholar 

  17. Arunkumar, S., Subramaniyaswamy, V., Devika, R., Logesh, R.: Generating visually meaningful encrypted image using image splitting technique. Int. J. Mech. Eng. Technol. 8(8), 361–368 (2017)

    Google Scholar 

  18. Saravanan, P., Arunkumar, S., Subramaniyaswamy, V., Logesh, R.: Enhanced web caching using bloom filter for local area networks. Int. J. Mech. Eng. Technol. 8(8), 211–217 (2017)

    Google Scholar 

  19. Senthilselvan, N., Udaya, Sree N., Medini, T., Subhakari, Mounika G., Subramaniyaswamy, V., Sivaramakrishnan, N., Logesh, R.: Keyword-aware recommender system based on user demographic attributes. Int. J. Mech. Eng. Technol. 8(8), 1466–1476 (2017)

    Google Scholar 

  20. Nilashi, M., Ibrahim, O., Bagherifard, K.: A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst. Appl. 92, 507–520 (2018)

    Article  Google Scholar 

  21. Nilashi, M., Bagherifard, K., Ibrahim, O., Alizadeh, H., Lasisi, A., Roozegar, N.: Collaborative filtering recommender systems. Res. J. Appl. Sci. Eng. Technol. 5, 4168–4182 (2013)

    Google Scholar 

  22. Pham, M.C., Cao, Y., Klamma, R., Jarke, M.: A clustering approach for collaborative filtering recommendation using social network analysis. J. Univers. Comput. Sci. 17(4), 583–604 (2011)

    Google Scholar 

  23. Gong, S.J.: A collaborative filtering recommendation algorithm based on user clustering and item clustering. J. Softw. 5(7) (2010)

    Google Scholar 

  24. Kushwaha, N., Vyas, O. P.: SemMovieRec: extraction of semantic features of DBpedia for recommender system. In: Proceedings of the 7th ACM India Computing Conference. p.13 (2014)

    Google Scholar 

  25. Zheng, X., Luo, Y., Xu, Z., Yu, O., Lu, L.: Tourism destination recommender system for the Cold start problem. KSII T. Internet Info. Syst. 10(7) (2016)

    Google Scholar 

  26. Hariri, N., Zheng, Y., Mobasher, B., Burke, R.: Context-aware recommendation based on review mining. Gen. Co-Chairs (2011)

    Google Scholar 

  27. Adomavicius, G., Tuzhilin, A.: Context-Aware Recommender Systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook. Springer, Boston, MA (2015)

    MATH  Google Scholar 

  28. Braunhofer, M., Ricci, F.: Selective contextual information acquisition in travel recommender systems. Inf Technol Tourism. (2017)

    Google Scholar 

  29. Bahramian, Z., Abbaspour, R.A., Claramunt, C.: A cold start context-aware recommender system for tour planning using artificial neural network and case based reasoning. Mob. Inf. Syst. (2017)

    Google Scholar 

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Acknowledgements

Authors thank the Science and Engineering Research Board for their financial support (YSS/2014/000718/ES). Authors also express their gratitude to SASTRA Deemed University for the infrastructure facilities and support provided to conduct the research.

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Authors and Affiliations

  1. School of Computing, SASTRA Deemed University, Thanjavur, 613401, India

    R. Logesh & V. Subramaniyaswamy

Authors
  1. R. Logesh
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  2. V. Subramaniyaswamy
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Corresponding author

Correspondence to R. Logesh .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Vignana Bharathi Institute of Technology, Hyderabad, Telangana, India

    Pradeep Kumar Mallick

  2. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Rangpo, India

    Akash Kumar Bhoi

  4. Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, Middlesex, United Kingdom

    Ahmed F. Zobaa

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Logesh, R., Subramaniyaswamy, V. (2019). Exploring Hybrid Recommender Systems for Personalized Travel Applications. In: Mallick, P., Balas, V., Bhoi, A., Zobaa, A. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-13-0617-4_52

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