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Trip Recommendation Using Location-Based Social Network: A Review

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International Conference on Artificial Intelligence Science and Applications (CAISA) (CAISA 2022)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1441))

Abstract

The travel industry is both a significant industry and a well-known recreation movement embraced by millions throughout the planet. One great errand for travelers is to plan and timetable visit schedules that involve the various dazzling Locations of Interest (LOIs) dependent on the extraordinary inclinations of a traveler. The mind-boggling errand of visit agenda proposal is additionally confounded by the need to fuse distinct genuine imperatives like restricted time for visiting, unsure traffic conditions, severe climate, group trips, lining times, and overcrowding. In this learning, we direct thorough writing examination of studies on visit schedule suggestions and present an overall scientific classification for visiting related examination. We will cover the Location of Interest (LOI) and sequence of LOIs studies that have been done to improve the traveling experiences of users. We talk about the whole cycle of visit schedule suggestion research covering: (i) information assortment and kinds of datasets; (ii) issue plans and suggested calculations/frameworks for individual travelers, gatherings of travelers, and different genuine contemplation; (iii) assessment strategies for looking at a visit to a recommended LOI; (iv) assessment strategies for comparing trip planned route recommendation algorithms; and (v) upcoming bearings and open issues in LOI and trip planned route recommendation research.

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Author information

Authors and Affiliations

  1. College of Software Engineering, Northeastern University, Hunnan, Shenyang, 110169, Liaoning, China

    Rizwan Abbas & Irshad Hussain

  2. Department of Management Science and Engineering, Dalian University of Technology, Ganjingzi, Dalian, 116620, Liaoning, China

    Gehad Abdullah Amran

  3. College of Computing and Information Technology, University of Tabuk, King Faisal Road, Tabuk, 71491, Saudi Arabia

    Sultan Trahib Alotaibi

  4. Department of Computer Science, Technique Leaders College, 14 October, Sana’a, 31220, Yemen

    Ali A. AL-Bakhrani

  5. Department of Software Engineering, School of Software Technology, Dalian University of Technology, Xuefu, Dalian, 116620, Liaoning, China

    Esmail Almosharea

  6. College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004, China

    Mohammed A. A. Al-qaness

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  1. Rizwan Abbas
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  2. Irshad Hussain
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  3. Gehad Abdullah Amran
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  4. Sultan Trahib Alotaibi
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  5. Ali A. AL-Bakhrani
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  6. Esmail Almosharea
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  7. Mohammed A. A. Al-qaness
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Corresponding authors

Correspondence to Rizwan Abbas or Sultan Trahib Alotaibi .

Editor information

Editors and Affiliations

  1. Galala University, Al Galala, Egypt

    Mohamed Abd Elaziz

  2. Faculty of Computer Science and Engineering, Galala University, Al Galala, Egypt

    Mohamed Medhat Gaber

  3. Galala University, Al Galala, Egypt

    Shaker El-Sappagh

  4. College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, China

    Mohammed A. A. Al-qaness

  5. Damietta University, Damietta, Egypt

    Ahmed A. Ewees

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Abbas, R. et al. (2023). Trip Recommendation Using Location-Based Social Network: A Review. In: Abd Elaziz, M., Medhat Gaber, M., El-Sappagh, S., Al-qaness, M.A.A., Ewees, A.A. (eds) International Conference on Artificial Intelligence Science and Applications (CAISA). CAISA 2022. Advances in Intelligent Systems and Computing, vol 1441. Springer, Cham. https://doi.org/10.1007/978-3-031-28106-8_8

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