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Exploring Hybrid Recommender Systems for Personalized Travel Applications

  • R. Logesh
  • V. Subramaniyaswamy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

Recommender systems Collaborative filtering Context-aware recommendations Clustering E-Toursim Hybrid systems 

Notes

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of ComputingSASTRA Deemed UniversityThanjavurIndia

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