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Hybrid User Clustering-Based Travel Planning System for Personalized Point of Interest Recommendation

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Mathematical Modeling, Computational Intelligence Techniques and Renewable Energy

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1287))

Abstract

In the recent times, the massive amount of user-generated data acquired from Internet has become the main source for recommendation generation process in various real-time personalization problems. Among various types of recommender systems, collaborative filtering-based approaches are found to be more effective in generating better recommendations. The recommendation models that are based on this collaborative filtering approach are used to predict items highly similar to the interest of an active target user. Thus, a new hybrid user clustering-based travel recommender system (HUCTRS) is proposed by integrating multiple swarm intelligence algorithms for better clustering. The proposed HUCTRS is experimentally assessed on the large-scale datasets to demonstrate its performance efficiency. The results obtained also proved the potential of proposed HUCTRS over traditional approaches by means of improved user satisfaction.

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Acknowledgements

The authors gratefully acknowledge the Science and Engineering Research Board (SERB), Department of Science and Technology, India, for the financial support through Mathematical Research Impact Centric Support (MATRICS) scheme (MTR/2019/000542). The authors also acknowledge SASTRA Deemed University, Thanjavur, for extending infrastructural support to carry out this research work.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India

    Logesh Ravi

  2. School of Computing, SASTRA Deemed University, Thanjavur, India

    V. Subramaniyaswamy

  3. School of Computer Science and Engineering, University of New South Wales, Kensington, Australia

    V. Vijayakumar

  4. School of Technology, Pandit Deendayal Petroleum University, Gandhinagar, India

    Rutvij H. Jhaveri & Jigarkumar Shah

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  1. Logesh Ravi
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  2. V. Subramaniyaswamy
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  3. V. Vijayakumar
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  4. Rutvij H. Jhaveri
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  5. Jigarkumar Shah
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Corresponding author

Correspondence to V. Subramaniyaswamy .

Editor information

Editors and Affiliations

  1. Department of Mathematics, School of Technology, Pandit Deendayal Petroleum University, Gandhinagar, India

    Manoj Sahni

  2. University of Technology Sydney, Sydney, NSW, Australia

    José M. Merigó

  3. Department of Mathematics, School of Technology, Pandit Deendayal Petroleum University, Gandhinagar, India

    Brajesh Kumar Jha

  4. Department of Management Control and Information Systems, University of Chile, Santiago, Chile

    Rajkumar Verma

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Ravi, L., Subramaniyaswamy, V., Vijayakumar, V., Jhaveri, R.H., Shah, J. (2021). Hybrid User Clustering-Based Travel Planning System for Personalized Point of Interest Recommendation. In: Sahni, M., Merigó, J.M., Jha, B.K., Verma, R. (eds) Mathematical Modeling, Computational Intelligence Techniques and Renewable Energy. Advances in Intelligent Systems and Computing, vol 1287. Springer, Singapore. https://doi.org/10.1007/978-981-15-9953-8_27

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